{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression\n",
    "\n",
    "In this lecture, I will bring together various techniques for feature engineering that we have covered in this course to tackle a regression problem. This would give you an idea of the end-to-end pipeline to build machine learning algorithms for regression.\n",
    "\n",
    "===================================================================================================\n",
    "\n",
    "## Real Life example: \n",
    "\n",
    "### Predicting Sale Price of Houses\n",
    "\n",
    "The problem at hand aims to predict the final sale price of homes based on different explanatory variables describing aspects of residential homes. Predicting house prices is useful to identify fruitful investments, or to determine whether the price advertised for a house is over or underestimated, before making a buying judgment.\n",
    "\n",
    "To download the House Price dataset go this website:\n",
    "https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data\n",
    "\n",
    "Scroll down to the bottom of the page, and click on the link 'train.csv', and then click the 'download' blue button towards the right of the screen, to download the dataset.\n",
    "Save it to a directory of your choice.\n",
    "\n",
    "**Note that you need to be logged in to Kaggle in order to download the datasets**.\n",
    "\n",
    "If you save it in the same directory from which you are running this notebook and name the file 'houseprice.csv' then you can load it the same way I will load it below.\n",
    "\n",
    "===================================================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## House Prices dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to handle datasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "\n",
    "# to divide train and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# feature scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# for tree binarisation\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "# to build the models\n",
    "from sklearn.linear_model import LinearRegression, Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "import xgboost as xgb\n",
    "\n",
    "# to evaluate the models\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "pd.pandas.set_option('display.max_columns', None)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       Norm     1Fam     2Story            7            5       2003   \n",
       "1       Norm     1Fam     1Story            6            8       1976   \n",
       "2       Norm     1Fam     2Story            7            5       2001   \n",
       "3       Norm     1Fam     2Story            7            5       1915   \n",
       "4       Norm     1Fam     2Story            8            5       2000   \n",
       "\n",
       "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1          1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3          1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          GLQ         706          Unf           0        150          856   \n",
       "1          ALQ         978          Unf           0        284         1262   \n",
       "2          GLQ         486          Unf           0        434          920   \n",
       "3          ALQ         216          Unf           0        540          756   \n",
       "4          GLQ         655          Unf           0        490         1145   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0       1710             1             0         2         1             3   \n",
       "1       1262             0             1         2         0             3   \n",
       "2       1786             1             0         2         1             3   \n",
       "3       1717             1             0         1         0             3   \n",
       "4       2198             1             0         2         1             4   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          Gd             8        Typ           0         NaN   \n",
       "1             1          TA             6        Typ           1          TA   \n",
       "2             1          Gd             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             9        Typ           1          TA   \n",
       "\n",
       "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd       2003.0          RFn           2         548         TA   \n",
       "1     Attchd       1976.0          RFn           2         460         TA   \n",
       "2     Attchd       2001.0          RFn           2         608         TA   \n",
       "3     Detchd       1998.0          Unf           3         642         TA   \n",
       "4     Attchd       2000.0          RFn           3         836         TA   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y           0           61              0          0   \n",
       "1         TA          Y         298            0              0          0   \n",
       "2         TA          Y           0           42              0          0   \n",
       "3         TA          Y           0           35            272          0   \n",
       "4         TA          Y         192           84              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0            0         0    NaN   NaN         NaN        0       2    2008   \n",
       "1            0         0    NaN   NaN         NaN        0       5    2007   \n",
       "2            0         0    NaN   NaN         NaN        0       9    2008   \n",
       "3            0         0    NaN   NaN         NaN        0       2    2006   \n",
       "4            0         0    NaN   NaN         NaN        0      12    2008   \n",
       "\n",
       "  SaleType SaleCondition  SalePrice  \n",
       "0       WD        Normal     208500  \n",
       "1       WD        Normal     181500  \n",
       "2       WD        Normal     223500  \n",
       "3       WD       Abnorml     140000  \n",
       "4       WD        Normal     250000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dataset\n",
    "data = pd.read_csv('houseprice.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Rec</td>\n",
       "      <td>468.0</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>144.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>108.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>923.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>791.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>928</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>20.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>602.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>926</td>\n",
       "      <td>678</td>\n",
       "      <td>0</td>\n",
       "      <td>1604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>360</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>StoneBr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>TwnhsE</td>\n",
       "      <td>1Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>263.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl        NAmes      Feedr   \n",
       "1         Lvl    AllPub    Corner       Gtl        NAmes       Norm   \n",
       "2         Lvl    AllPub    Inside       Gtl      Gilbert       Norm   \n",
       "3         Lvl    AllPub    Inside       Gtl      Gilbert       Norm   \n",
       "4         HLS    AllPub    Inside       Gtl      StoneBr       Norm   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       Norm     1Fam     1Story            5            6       1961   \n",
       "1       Norm     1Fam     1Story            6            6       1958   \n",
       "2       Norm     1Fam     2Story            5            5       1997   \n",
       "3       Norm     1Fam     2Story            6            6       1998   \n",
       "4       Norm   TwnhsE     1Story            8            5       1992   \n",
       "\n",
       "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0          1961     Gable  CompShg     VinylSd     VinylSd       None   \n",
       "1          1958       Hip  CompShg     Wd Sdng     Wd Sdng    BrkFace   \n",
       "2          1998     Gable  CompShg     VinylSd     VinylSd       None   \n",
       "3          1998     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "4          1992     Gable  CompShg     HdBoard     HdBoard       None   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0         0.0        TA        TA     CBlock       TA       TA           No   \n",
       "1       108.0        TA        TA     CBlock       TA       TA           No   \n",
       "2         0.0        TA        TA      PConc       Gd       TA           No   \n",
       "3        20.0        TA        TA      PConc       TA       TA           No   \n",
       "4         0.0        Gd        TA      PConc       Gd       TA           No   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          Rec       468.0          LwQ       144.0      270.0        882.0   \n",
       "1          ALQ       923.0          Unf         0.0      406.0       1329.0   \n",
       "2          GLQ       791.0          Unf         0.0      137.0        928.0   \n",
       "3          GLQ       602.0          Unf         0.0      324.0        926.0   \n",
       "4          ALQ       263.0          Unf         0.0     1017.0       1280.0   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        TA          Y      SBrkr       896         0             0   \n",
       "1    GasA        TA          Y      SBrkr      1329         0             0   \n",
       "2    GasA        Gd          Y      SBrkr       928       701             0   \n",
       "3    GasA        Ex          Y      SBrkr       926       678             0   \n",
       "4    GasA        Ex          Y      SBrkr      1280         0             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0        896           0.0           0.0         1         0             2   \n",
       "1       1329           0.0           0.0         1         1             3   \n",
       "2       1629           0.0           0.0         2         1             3   \n",
       "3       1604           0.0           0.0         2         1             3   \n",
       "4       1280           0.0           0.0         2         0             2   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          TA             5        Typ           0         NaN   \n",
       "1             1          Gd             6        Typ           0         NaN   \n",
       "2             1          TA             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             5        Typ           0         NaN   \n",
       "\n",
       "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd       1961.0          Unf         1.0       730.0         TA   \n",
       "1     Attchd       1958.0          Unf         1.0       312.0         TA   \n",
       "2     Attchd       1997.0          Fin         2.0       482.0         TA   \n",
       "3     Attchd       1998.0          Fin         2.0       470.0         TA   \n",
       "4     Attchd       1992.0          RFn         2.0       506.0         TA   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y         140            0              0          0   \n",
       "1         TA          Y         393           36              0          0   \n",
       "2         TA          Y         212           34              0          0   \n",
       "3         TA          Y         360           36              0          0   \n",
       "4         TA          Y           0           82              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC  Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0          120         0    NaN  MnPrv         NaN        0       6    2010   \n",
       "1            0         0    NaN    NaN        Gar2    12500       6    2010   \n",
       "2            0         0    NaN  MnPrv         NaN        0       3    2010   \n",
       "3            0         0    NaN    NaN         NaN        0       6    2010   \n",
       "4          144         0    NaN    NaN         NaN        0       1    2010   \n",
       "\n",
       "  SaleType SaleCondition  \n",
       "0       WD        Normal  \n",
       "1       WD        Normal  \n",
       "2       WD        Normal  \n",
       "3       WD        Normal  \n",
       "4       WD        Normal  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset for submission (the one on which our model will be evaluated by Kaggle)\n",
    "# it contains exactly the same variables, but not the target\n",
    "\n",
    "submission = pd.read_csv('house_price_submission.csv')\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The House Price dataset is bigger than the Titanic dataset. It contains more variables for each one of the houses. Thus, manual inspection of each one of them is a bit time demanding. Therefore, here instead of deciding variable by variable what is the best way to proceed, I will try to automate the feature engineering pipeline, making some a priori decisions on when I will apply one technique or the other, and then expanding them to the entire dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types of variables (section 2)\n",
    "\n",
    "Let's go ahead and find out what types of variables there are in this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 int64\n",
       "MSSubClass         int64\n",
       "MSZoning          object\n",
       "LotFrontage      float64\n",
       "LotArea            int64\n",
       "Street            object\n",
       "Alley             object\n",
       "LotShape          object\n",
       "LandContour       object\n",
       "Utilities         object\n",
       "LotConfig         object\n",
       "LandSlope         object\n",
       "Neighborhood      object\n",
       "Condition1        object\n",
       "Condition2        object\n",
       "BldgType          object\n",
       "HouseStyle        object\n",
       "OverallQual        int64\n",
       "OverallCond        int64\n",
       "YearBuilt          int64\n",
       "YearRemodAdd       int64\n",
       "RoofStyle         object\n",
       "RoofMatl          object\n",
       "Exterior1st       object\n",
       "Exterior2nd       object\n",
       "MasVnrType        object\n",
       "MasVnrArea       float64\n",
       "ExterQual         object\n",
       "ExterCond         object\n",
       "Foundation        object\n",
       "                  ...   \n",
       "BedroomAbvGr       int64\n",
       "KitchenAbvGr       int64\n",
       "KitchenQual       object\n",
       "TotRmsAbvGrd       int64\n",
       "Functional        object\n",
       "Fireplaces         int64\n",
       "FireplaceQu       object\n",
       "GarageType        object\n",
       "GarageYrBlt      float64\n",
       "GarageFinish      object\n",
       "GarageCars         int64\n",
       "GarageArea         int64\n",
       "GarageQual        object\n",
       "GarageCond        object\n",
       "PavedDrive        object\n",
       "WoodDeckSF         int64\n",
       "OpenPorchSF        int64\n",
       "EnclosedPorch      int64\n",
       "3SsnPorch          int64\n",
       "ScreenPorch        int64\n",
       "PoolArea           int64\n",
       "PoolQC            object\n",
       "Fence             object\n",
       "MiscFeature       object\n",
       "MiscVal            int64\n",
       "MoSold             int64\n",
       "YrSold             int64\n",
       "SaleType          object\n",
       "SaleCondition     object\n",
       "SalePrice          int64\n",
       "Length: 81, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's inspect the type of variables in pandas\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a mixture of categorical and numerical variables. Numerical are those of type int and float. Categorical those of type object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of House Id labels:  1460\n",
      "Number of Houses in the Dataset:  1460\n"
     ]
    }
   ],
   "source": [
    "print('Number of House Id labels: ', len(data.Id.unique()))\n",
    "print('Number of Houses in the Dataset: ', len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Id is a unique identifier for each of the houses. Thus this is not a variable that we can use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 43 categorical variables\n"
     ]
    }
   ],
   "source": [
    "# find categorical variables\n",
    "categorical = [var for var in data.columns if data[var].dtype=='O']\n",
    "print('There are {} categorical variables'.format(len(categorical)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 38 numerical variables\n"
     ]
    }
   ],
   "source": [
    "# find numerical variables\n",
    "numerical = [var for var in data.columns if data[var].dtype!='O']\n",
    "print('There are {} numerical variables'.format(len(numerical)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find discrete variables\n",
    "\n",
    "To identify discrete variables, I will select from all the numerical ones, those that contain a finite and small number of distinct values. See below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass  values:  [ 60  20  70  50 190  45  90 120  30  85  80 160  75 180  40]\n",
      "OverallQual  values:  [ 7  6  8  5  9  4 10  3  1  2]\n",
      "OverallCond  values:  [5 8 6 7 4 2 3 9 1]\n",
      "BsmtFullBath  values:  [1 0 2 3]\n",
      "BsmtHalfBath  values:  [0 1 2]\n",
      "FullBath  values:  [2 1 3 0]\n",
      "HalfBath  values:  [1 0 2]\n",
      "BedroomAbvGr  values:  [3 4 1 2 0 5 6 8]\n",
      "KitchenAbvGr  values:  [1 2 3 0]\n",
      "TotRmsAbvGrd  values:  [ 8  6  7  9  5 11  4 10 12  3  2 14]\n",
      "Fireplaces  values:  [0 1 2 3]\n",
      "GarageCars  values:  [2 3 1 0 4]\n",
      "PoolArea  values:  [  0 512 648 576 555 480 519 738]\n",
      "MoSold  values:  [ 2  5  9 12 10  8 11  4  1  7  3  6]\n",
      "YrSold  values:  [2008 2007 2006 2009 2010]\n",
      "There are 15 discrete variables\n"
     ]
    }
   ],
   "source": [
    "# let's visualise the values of the discrete variables\n",
    "discrete = []\n",
    "for var in numerical:\n",
    "    if len(data[var].unique())<20:\n",
    "        print(var, ' values: ', data[var].unique())\n",
    "        discrete.append(var)\n",
    "        \n",
    "print('There are {} discrete variables'.format(len(discrete)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types of problems within the variables (section 3)\n",
    "\n",
    "#### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage 0.177397260274\n",
      "Alley 0.937671232877\n",
      "MasVnrType 0.00547945205479\n",
      "MasVnrArea 0.00547945205479\n",
      "BsmtQual 0.0253424657534\n",
      "BsmtCond 0.0253424657534\n",
      "BsmtExposure 0.0260273972603\n",
      "BsmtFinType1 0.0253424657534\n",
      "BsmtFinType2 0.0260273972603\n",
      "Electrical 0.000684931506849\n",
      "FireplaceQu 0.472602739726\n",
      "GarageType 0.0554794520548\n",
      "GarageYrBlt 0.0554794520548\n",
      "GarageFinish 0.0554794520548\n",
      "GarageQual 0.0554794520548\n",
      "GarageCond 0.0554794520548\n",
      "PoolQC 0.995205479452\n",
      "Fence 0.807534246575\n",
      "MiscFeature 0.96301369863\n"
     ]
    }
   ],
   "source": [
    "# let's visualise the percentage of missing values\n",
    "for var in data.columns:\n",
    "    if data[var].isnull().sum()>0:\n",
    "        print(var, data[var].isnull().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alley [nan 'Grvl' 'Pave']\n",
      "PoolQC [nan 'Ex' 'Fa' 'Gd']\n",
      "Fence [nan 'MnPrv' 'GdWo' 'GdPrv' 'MnWw']\n",
      "MiscFeature [nan 'Shed' 'Gar2' 'Othr' 'TenC']\n"
     ]
    }
   ],
   "source": [
    "# let's inspect the type of those variables with a lot of missing information\n",
    "for var in data.columns:\n",
    "    if data[var].isnull().mean()>0.80:\n",
    "        print(var, data[var].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few variables with missing data. The ones with a lot of missing data are categorical variables. We will need to fill those out. See below.\n",
    "\n",
    "#### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage',\n",
       " 'LotArea',\n",
       " 'YearBuilt',\n",
       " 'YearRemodAdd',\n",
       " 'MasVnrArea',\n",
       " 'BsmtFinSF1',\n",
       " 'BsmtFinSF2',\n",
       " 'BsmtUnfSF',\n",
       " 'TotalBsmtSF',\n",
       " '1stFlrSF',\n",
       " '2ndFlrSF',\n",
       " 'LowQualFinSF',\n",
       " 'GrLivArea',\n",
       " 'GarageYrBlt',\n",
       " 'GarageArea',\n",
       " 'WoodDeckSF',\n",
       " 'OpenPorchSF',\n",
       " 'EnclosedPorch',\n",
       " '3SsnPorch',\n",
       " 'ScreenPorch',\n",
       " 'MiscVal']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous = [var for var in numerical if var not in discrete and var not in ['Id', 'SalePrice']]\n",
    "continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA38AAAF3CAYAAAAVe/LLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3X+cXVV97//XO4EGLqBC1fmGX4ZWVCIUqLlUKtfvRGqx\ncAva9lpS9fojF0S5I16pgtDrj2vT4g+wt7SAoaHGXgnirxIFrYgMlm+tCAgiiUBagoYvQi1USCyp\niZ/7x9lDhzCTTJI5s+fMeT0fj/M4e6991j6flTOZM5+91l4rVYUkSZIkaWab1XYAkiRJkqTuM/mT\nJEmSpD5g8idJkiRJfcDkT5IkSZL6gMmfJEmSJPUBkz9JkiRJ6gMmf5IkSZLUB0z+JEmSJKkPmPxJ\nkiRJUh8w+ZMkSZKkPrBL2wHsjGc+85k1b968tsOQptyGDRvYY4892g5DmlK33HLLj6rqWW3H0Stm\n2ndkP/3es60zk22dudpu7/Z8P/Z08jdv3jxuvvnmtsOQptzw8DCDg4NthyFNqST3tR1DL5lp35H9\n9HvPts5MtnXmaru92/P96LBPSZIkSeoDJn+SJEmS1AdM/iRJkiSpD5j8SZIkSVIfMPmTJEmSpD5g\n8idJkiRJfcDkT5IkSZL6gMmfJElTKMluSW5KcnuSO5O8vyl/X5L7k9zWPI4fVefdSdYkuSvJce1F\nL0nqZT29yLskST1oI/CyqlqfZFfgxiRfao59tKo+MvrFSeYDJwMvBPYFvprkeVW1eUqjliT1PHv+\npB4yNDTEbrvtxsKFC9ltt90YGhpqOyRJ26k61je7uzaP2kqVk4ArqmpjVd0LrAGO6nKYkqQZyJ4/\nqUcMDQ1xySWX8MEPfpD58+ezatUqzjrrLAAuvPDClqOTtD2SzAZuAZ4L/HlVfTPJbwBDSf4rcDNw\nZlU9AuwH/P2o6uuaMkmStkuqtnaxcXpbsGBB3XzzzW2HIU2J3XbbjT/6oz/iHe94B8PDwwwODnLB\nBRdwzjnn8Pjjj7cdntR1SW6pqgVtxzGZkjwD+DwwBPwT8CM6vYAfAOZW1ZuS/Bnw91X1f5o6y4Av\nVdVnxjjfqcCpAAMDAy+64oorpqYhU2D9+vXsueeebYcxJWzrzGRbZ66227tw4cIJfz/a8yf1iI0b\nN3Laaac9qey0007jzDPPbCkiSTurqv4lyfXAK0bf65fkUuCLze79wAGjqu3flI11vqXAUuhcIB0c\nHOxG2K0YuejVD2zrzGRbZ65eaq/Jn9Qj5syZwyWXXMI73vGOJ8ouueQS5syZ02JUkrZXkmcBP20S\nv92BlwMfTDK3qh5oXvYq4LvN9krg8iQX0Jnw5WDgpqmOeyaYd/bVO32OteedMAmRSFI7TP6kHnHK\nKac8cY/f/PnzueCCCzjrrLOe0hsoadqbCyxv7vubBVxZVV9M8ldJjqAz7HMt8GaAqrozyZXAKmAT\ncLozfUqSdoTJn9QjRiZ1Oeecc9i4cSNz5szhtNNOc7IXqcdU1XeAI8cof91W6iwBlnQzLknSzOdS\nD1IPufDCC3n88ce5/vrrefzxx038JEmSNGEmf5IkSZLUB0z+JEmSJKkPmPxJkiRJUh8w+ZMkSZKk\nPmDyJ0mSJEl9oGvJX5LdktyU5PYkdyZ5f1O+T5Jrk9zTPO89qs67k6xJcleS47oVmyRJkiT1m272\n/G0EXlZVhwNHAK9I8mLgbOC6qjoYuK7ZJ8l84GTghcArgIuaBXAlSZIkSTupa8lfdaxvdndtHgWc\nBCxvypcDr2y2TwKuqKqNVXUvsAY4qlvxSZIkSVI/6eo9f0lmJ7kNeAi4tqq+CQxU1QPNS34IDDTb\n+wE/GFV9XVMmSZIkSdpJu3Tz5FW1GTgiyTOAzyc5dIvjlaS255xJTgVOBRgYGGB4eHiywpV6xvr1\n6/3ZlyRJ0nbpavI3oqr+Jcn1dO7lezDJ3Kp6IMlcOr2CAPcDB4yqtn9TtuW5lgJLARYsWFCDg4Nd\njV2ajoaHh/FnX5IkSdujm7N9Pqvp8SPJ7sDLge8BK4HXNy97PXBVs70SODnJnCQHAQcDN3UrPkmS\nJEnqJ93s+ZsLLG9m7JwFXFlVX0zyDeDKJIuB+4BXA1TVnUmuBFYBm4DTm2GjkiRJkqSd1LXkr6q+\nAxw5Rvk/A8eOU2cJsKRbMUmSJElSv+rqbJ+SJEmSpOnB5E+SJEmS+oDJnyRJkiT1AZM/SZIkSeoD\nJn+SJEmS1AdM/iRJkiSpD5j8SZIkSVIfMPmTJEmSpD5g8idJkiRJfcDkT5IkSZL6gMmfJEmSJPUB\nkz9JkiRJ6gMmf5IkSZLUB0z+JEmSJKkPmPxJkiRJUh8w+ZMkSZKkPmDyJ0mSJEl9wORPkiRJkvqA\nyZ8kSZIk9QGTP0mSJEnqAyZ/kiRJktQHTP4kSZpCSXZLclOS25PcmeT9Tfk+Sa5Nck/zvPeoOu9O\nsibJXUmOay96SVIvM/mTJGlqbQReVlWHA0cAr0jyYuBs4LqqOhi4rtknyXzgZOCFwCuAi5LMbiVy\nSVJPM/mTJGkKVcf6ZnfX5lHAScDypnw58Mpm+yTgiqraWFX3AmuAo6YwZEnSDGHyJ0nSFEsyO8lt\nwEPAtVX1TWCgqh5oXvJDYKDZ3g/4wajq65oySZK2yy5tByBJUr+pqs3AEUmeAXw+yaFbHK8ktb3n\nTXIqcCrAwMAAw8PDkxHutLB+/fqdbs+Zh23a6Tim4t90MtraK2zrzNRPbYXeaq/JnyRJLamqf0ly\nPZ17+R5MMreqHkgyl06vIMD9wAGjqu3flI11vqXAUoAFCxbU4OBg12KfasPDw+xse95w9tU7Hcfa\n1+xcDBMxGW3tFbZ1ZuqntkJvtddhn5IkTaEkz2p6/EiyO/By4HvASuD1zcteD1zVbK8ETk4yJ8lB\nwMHATVMbtSRpJrDnT5KkqTUXWN7M2DkLuLKqvpjkG8CVSRYD9wGvBqiqO5NcCawCNgGnN8NGJUna\nLiZ/kiRNoar6DnDkGOX/DBw7Tp0lwJIuhzat3XH/jydl2KYk9TOHfUqSJElSHzD5kyRJkqQ+YPIn\nSZIkSX3A5E+SJEmS+oDJnyRJkiT1AZM/SZIkSeoDJn+SJEmS1AdM/iRJkiSpD5j8SZIkSVIfMPmT\nJEmSpD5g8idJkiRJfaBryV+SA5Jcn2RVkjuTnNGUvy/J/Uluax7Hj6rz7iRrktyV5LhuxSZJkiRJ\n/WaXLp57E3BmVd2aZC/gliTXNsc+WlUfGf3iJPOBk4EXAvsCX03yvKra3MUYJUmSJKkvdK3nr6oe\nqKpbm+3HgNXAflupchJwRVVtrKp7gTXAUd2KT5IkSZL6yZTc85dkHnAk8M2maCjJd5JclmTvpmw/\n4Aejqq1j68miJEmSJGmCujnsE4AkewKfBd5eVY8muRj4AFDN8/nAm7bjfKcCpwIMDAwwPDw86TFL\n09369ev92ZckSdJ26Wryl2RXOonfJ6vqcwBV9eCo45cCX2x27wcOGFV9/6bsSapqKbAUYMGCBTU4\nONiV2KXpbHh4GH/2JUmStD26OdtngGXA6qq6YFT53FEvexXw3WZ7JXBykjlJDgIOBm7qVnySJEmS\n1E+62fP3EuB1wB1JbmvKzgEWJTmCzrDPtcCbAarqziRXAqvozBR6ujN9SpIkSdLk6FryV1U3Ahnj\n0DVbqbMEWNKtmCRJkiSpX03JbJ+SJEmSpHaZ/EmSJElSHzD5kyRJkqQ+YPInSZIkSX3A5E+SJEmS\n+oDJnyRJkiT1AZM/SZIkSeoDJn+SJEmS1AdM/iRJkiSpD5j8SZIkSVIfMPmTJEmSpD5g8idJkiRJ\nfcDkT5IkSZL6gMmfJEmSJPUBkz9JkiRJ6gMmf5IkSZLUB0z+JEmaQkkOSHJ9klVJ7kxyRlP+viT3\nJ7mteRw/qs67k6xJcleS49qLXpLUy3ZpOwBJkvrMJuDMqro1yV7ALUmubY59tKo+MvrFSeYDJwMv\nBPYFvprkeVW1eUqjliT1PHv+JEmaQlX1QFXd2mw/BqwG9ttKlZOAK6pqY1XdC6wBjup+pJKkmcbk\nT5KkliSZBxwJfLMpGkrynSSXJdm7KdsP+MGoauvYerIoSdKYHPYpSVILkuwJfBZ4e1U9muRi4ANA\nNc/nA2/aznOeCpwKMDAwwPDw8KTG3KaB3eHMwza1HcaU/JuuX79+Rn12W2NbZ6Z+aiv0VntN/iRJ\nmmJJdqWT+H2yqj4HUFUPjjp+KfDFZvd+4IBR1fdvyp6iqpYCSwEWLFhQg4ODkx57Wy785FWcf0f7\nf7asfc1g199jeHiYmfTZbY1tnZn6qa3QW+112KckSVMoSYBlwOqqumBU+dxRL3sV8N1meyVwcpI5\nSQ4CDgZumqp4JUkzR/uX0CRJ6i8vAV4H3JHktqbsHGBRkiPoDPtcC7wZoKruTHIlsIrOTKGnO9On\nJGlHmPxJkrSDkrwEuK2qNiR5LfDLwP+uqvvGq1NVNwIZ49A1W6mzBFiys/FKkvqbwz4lSdpxFwM/\nSXI4cCbwD8An2g1JkqSxmfxJkrTjNlVV0VmL78+q6s+BvVqOSZKkMTnsU5KkHfdYkncDrwVemmQW\nsGvLMUmSNCZ7/iRJ2nG/C2wEFlfVD+ksw/DhdkOSJGls9vxJkrQDkswGVlTVwpGyqvo+3vMnSZqm\n7PmTJGkHNMst/CzJ09uORZKkibDnT5KkHbeeznp91wIbRgqr6m3thSRJ0thM/iRJ2nGfax6SJE17\nJn+SJO2gqlqeZHfgwKq6q+14JEnaGu/5kyRpByX5TeA24MvN/hFJVrYblSRJYzP5kyRpx70POAr4\nF4Cqug34hTYDkiRpPCZ/kiTtuJ9W1Y+3KPtZK5FIkrQN3vMnSdKOuzPJ7wGzkxwMvA34u5ZjkiRp\nTPb8SZK044aAFwIbgRXAo8DbW41IkqRx2PMnSdIOqqqfAOc2D0mSpjWTP0mSdlCSLwC1RfGPgZuB\nj1XV41MflSRJY3PYpyRJO+4fgfXApc3jUeAx4HnNviRJ00bXev6SHAB8Ahigc1V0aVX97yT7AJ8C\n5gFrgVdX1SNNnXcDi4HNwNuq6m+6FZ8kSZPgV6vqP47a/0KSb1XVf0xyZ2tRSZI0hm72/G0Czqyq\n+cCLgdOTzAfOBq6rqoOB65p9mmMn07lx/hXARUlmdzE+SZJ21p5JDhzZabb3bHb/rZ2QJEkaW9d6\n/qrqAeCBZvuxJKuB/YCTgMHmZcuBYeCspvyKqtoI3JtkDZ2Fc7/RrRglSdpJZwI3JvkHIMBBwFuT\n7EHnO06SpGljSiZ8STIPOBL4JjDQJIYAP6QzLBQ6ieHfj6q2rimTJGlaqqprmvX9XtAU3TVqkpc/\naSksSZLG1PXkL8mewGeBt1fVo0meOFZVlWTLWdK2db5TgVMBBgYGGB4ensRopd6wfv16f/al6eNF\ndO5j3wU4PAlV9Yl2Q5Ik6am6mvwl2ZVO4vfJqvpcU/xgkrlV9UCSucBDTfn9wAGjqu/flD1JVS0F\nlgIsWLCgBgcHuxW+NG0NDw/jz77UviR/BfwicBudycqgM8mZyd8MNe/sqyflPGvPO2FSziNJ26Ob\ns30GWAasrqoLRh1aCbweOK95vmpU+eVJLgD2BQ4GbupWfJIkTYIFwPyq2q5RLJIktaGbPX8vAV4H\n3JHktqbsHDpJ35VJFgP3Aa8GqKo7k1wJrKIzU+jpVbX5qaeVJGna+C7w/9BMcCZJ0nTWzdk+b6Qz\n89lYjh2nzhJgSbdikiRpkj0TWJXkJmDjSGFVndheSJIkjW1KZvuUJGmGel/bAUiSNFETWuQ9Ha9N\n8p5m/8AkR3U3NEmSprequgFYC+zabH8LuLXVoCRJGseEkj/gIuBoYFGz/xjw512JSJKkHpHkFOAz\nwMeaov2Av24vIkmSxjfR5O9Xqup04HGAqnoE+LmuRSVJUm84nc4EZ48CVNU9wLNbjUiSpHFMNPn7\naZLZdNYuIsmzgJ91LSpJknrDxqr6t5GdJLvQfFdKkjTdTDT5+1Pg88CzkywBbgT+qGtRSZLUG25I\ncg6we5KXA58GvtByTJIkjWlCs31W1SeT3EJniYYAr6yq1V2NTJKk6e9sYDFwB/Bm4BrgL1qNSJKk\ncUwo+UuyD/AQsGJU2a5V9dNuBSZJ0nRXVT8DLgUubb4r968qh31KkqaliQ77vBX4J+Bu4J5me22S\nW5O8qFvBSZI0nSUZTvK0JvG7hU4S+NG245IkaSwTTf6uBY6vqmdW1c8DvwF8EXgrnWUgJEnqR0+v\nqkeB3wI+UVW/QucWiXElOSDJ9UlWJbkzyRlN+T5Jrk1yT/O896g6706yJsldSY7raoskSTPWRJO/\nF1fV34zsVNVXgKOr6u+BOV2JTNJTrFixgkMPPZRjjz2WQw89lBUrVmy7kqRu2iXJXODVdC6KTsQm\n4Myqmg+8GDg9yXw69w9eV1UHA9c1+zTHTgZeCLwCuKiZgVuSpO0yoXv+gAeSnAVc0ez/LvBg8+Xj\nkg/SFFixYgXnnnsuy5YtY/PmzcyePZvFixcDsGjRopajk/rW/wL+Brixqr6V5Bfo3B4xrqp6AHig\n2X4syWo6i8OfBAw2L1sODANnNeVXVNVG4N4ka4CjgG9MemskSTPaRHv+fg/YH/jr5nFgUzabztVO\nSV22ZMkSli1bxsKFC9lll11YuHAhy5YtY8mSJW2HJvWtqvp0Vf1SVb212f/HqvrtidZPMg84Evgm\nMNAkhgA/BAaa7f2AH4yqtq4pkyRpu0x0qYcfAUPjHF4zeeFIGs/q1as55phjnlR2zDHHsHq1q65I\nbUnyIeAPgX8Fvgz8EvA/qur/TKDunsBngbdX1aNJnjhWVZVku2cNTXIqcCrAwMAAw8PD23uKaWtg\ndzjzsE1thzFptvbZrF+/fkZ9dltjW2emfmor9FZ7J7rUw7OAd9G532C3kfKqelmX4pK0hUMOOYQb\nb7yRhQsXPlF24403csghh7QYldT3fr2q3pXkVcBaOhO/fB3YavKXZFc6id8nq+pzTfGDSeZW1QPN\nfYQPNeX3AweMqr5/U/YUVbUUWAqwYMGCGhwc3KFGTUcXfvIqzr9jonerTH9rXzM47rHh4WFm0me3\nNbZ1ZuqntkJvtXeiwz4/CXwPOAh4P50vuG91KSZJYzj33HNZvHgx119/PZs2beL6669n8eLFnHvu\nuW2HJvWzkWzkBODTVfXjbVVIp4tvGbC6qi4YdWgl8Ppm+/XAVaPKT04yJ8lBwMHATZMRvCSpv0z0\nEtrPV9WyJGdU1Q3ADUlM/qQpNDKpy9DQEKtXr+aQQw5hyZIlTvYiteuLSb5HZ9jnW5qRMo9vo85L\ngNcBdyS5rSk7BzgPuDLJYuA+mnvqq+rOJFcCq+jMFHp6VW2e/KZIkma6iSZ/P22eH0hyAvD/A/t0\nJyRJ41m0aBGLFi3qqeEF0kxWVWc39/39uKo2J9lAZ3bOrdW5Ecg4h8dcI7CqlgDO7iRJ2ikTTf7+\nMMnTgTOBC4GnAW/vWlSSJPWOfYFfS7LbqLJPtBWMJEnjmeg9f49U1Y+r6rtVtbCqXgQ83M3AJD3V\n0NAQu+22GwsXLmS33XZjaGi8SXglTYUk76VzUfRCYCHwIeDEVoOSJGkcE+35uxD45QmUSeqSoaEh\nLrnkEj74wQ8yf/58Vq1axVlnnQXAhRde2HJ0Ut/6HeBw4NtV9cYkA2xjpk9Jktqy1eQvydHArwLP\nSvKOUYeeRmeBd0lT5NJLL+WDH/wg73jHOxgeHuYd7+j8lzznnHNM/qT2/GtV/SzJpiRPo7M8wwHb\nqiRJUhu2Nezz54A96SSJe416PErnaqekKbJx40ZOO+20J5WddtppbNy4saWIJAE3J3kGcClwC3Ar\n8I12Q5IkaWxb7fkbtazDx6vqvimKSdIY5syZwyWXXPJEjx/AJZdcwpw5c1qMSupvVfXWZvOSJF8G\nnlZV32kzJkmSxjPRe/7mJFkKzBtdp6pe1o2gJD3VKaec8sQ9fvPnz+eCCy7grLPOekpvoKSpleS3\ngGOAAm4ETP4kSdPSRJO/TwOXAH8BuLCs1IILL7yQu+++m9///d+nqkjCy1/+cu/3k1qU5CLgucCK\npujNSX6tqk5vMSxJksY00eRvU1Vd3NVIJG3VihUruOeee7juuuvYvHkzs2fPZvHixaxYsYJFixa1\nHZ7Ur14GHFJVBZBkOXBnuyFJkjS2ia7z94Ukb00yN8k+I4+uRibpSZYsWcKyZctYuHAhu+yyCwsX\nLmTZsmUsWbKk7dCkfrYGOHDU/gFNmSRJ085Ee/5e3zy/c1RZAb8wueFIGs/q1as55phjnlR2zDHH\nsHr16pYikkRnBuzVSW6i8714FJ0ZQFcCVJULvkuSpo0JJX9VdVC3A5G0dYcccgg33ngjCxcufKLs\nxhtv5JBDDmkxKqnvvaftACRJmqgJJX9JdgXeAry0KRoGPlZVP+1SXJK2cO6557J48WKWLVvG5s2b\nuf7661m8eLHDPqUWNUsiSZLUEyY67PNiYFfgomb/dU3Zf+tGUJKeamRSl6GhIVavXs0hhxzCkiVL\nnOxFkiRJEzLR5O8/VtXho/a/luT2bgQkaXyLFi1i0aJFDA8PMzg42HY4kiRJ6iETne1zc5JfHNlJ\n8gu43p805VasWMGhhx7Ksccey6GHHsqKFSu2XUnSpEtyXfP8wbZjkSRpoiba8/dO4Pok/wgEeA7w\nxq5FJekpVqxYwbnnnvvEPX8j6/wBDv2Upt7cJL8KnJjkCjrfjU+oqlvbCUuSpPFtM/lLMgv4V+Bg\n4PlN8V1VtbGbgUl6stHr/I0M+1y2bBlDQ0Mmf9LUew/wP4H9gQu2OFZ0Fn+XJGla2WbyV1U/S/Ln\nVXUk8J0piEnSGFznT5o+quozwGeS/M+q+kDb8UiSNBETvefvuiS/nSTbfqmkbhhZ52801/mT2lVV\nH0hyYpKPNI//3HZMkiSNZ6LJ35uBTwMbkzya5LEkj3YxLklbGFnn7/rrr2fTpk1PrPN37rnnth2a\n1LeS/DFwBrCqeZyR5I/ajUqSpLFNaMKXqtqr24FI2jrX+ZOmpROAI6rqZwBJlgPfBs5pNSpJksaw\n1Z6/JP991PYLux+OJEk95xmjtp/eWhSSJG3DtoZ9vmnU9l91MxBJW7dixQrOOOMMNmzYQFWxYcMG\nzjjjDNf6k9r1x8C3k3y86fW7BVjSckySJI1povf8wRZrGG3zxcllSR5K8t1RZe9Lcn+S25rH8aOO\nvTvJmiR3JTlue95L6gfvete7mD17Npdddhlf+cpXuOyyy5g9ezbvete72g5N6ltVtQJ4MfA54LPA\n0VX1qXajkiRpbNu65+8ZSV5FJ0l8WpLfGn2wqj63lbofB/4M+MQW5R+tqo+MLkgyHzgZeCGwL/DV\nJM+rqs3bboLUH9atW8fZZ5/9pHv+3vCGN3Deeee1HZrU16rqAWBl23FIkrQt20r+bgBObLa/Dvzm\nqGNF50rnmKrq60nmTTCOk4ArmoXj702yBjgK+MYE60t94aKLLmKfffYBYMOGDVx00UUtRyRJkqRe\nsdXkr6reCJDkoKq6d/SxJAft4HsOJfmvwM3AmVX1CLAf8PejXrOuKZPUmDVrFuvXr+e9730v8+fP\nZ9WqVbzzne9k1qztGb0tSZKkfjWhpR7o3Mfwy1uUfQZ40Xa+38XAB+j0Gn4AOJ8nTyqzTUlOBU4F\nGBgYYHh4eDtDkHrTz372M/bYYw8+/OEP89BDD/HsZz+b3XffnQ0bNvj/QGpBktnAnVX1grZjkSRp\nIraa/CV5AZ378J6+xf1+TwN22943q6oHR537UuCLze79wAGjXrp/UzbWOZYCSwEWLFhQg4OD2xuG\n1LPe9ra3sXLlSh566CF+/ud/nje+8Y388R//Mf4/kKZeVW1uJik7sKq+33Y8kiRty7Z6/p4P/Gc6\naxiNvt/vMeCU7X2zJHObG+MBXgWMzAS6Erg8yQV0Jnw5GLhpe88vzWT7778/f/mXf8nll1/O5s2b\nmT17Nr/3e7/H/vvv33ZoUj/bG7gzyU3AhpHCqjpx/CqSJLVjW/f8XQVcleToqtquyVeSrAAGgWcm\nWQe8FxhMcgSdYZ9rgTc373NnkiuBVcAm4HRn+pSe7EMf+hCnnXYaxx13HD/96U/Zdddd2X333bnk\nkkvaDk3qZ/+z7QAkSZqoid7z94Mknwde0uz/LXBGVa0br0JVLRqjeNlWXr8EF8aVtmrOnDnss88+\nfP/732e//fZjw4YN264kqWuq6oYkzwEOrqqvJvkPwOy245IkaSwTnSbwL+kMzdy3eXyhKZM0RZYs\nWcKnPvUp7r33Xq677jruvfdePvWpT7FkiddMpLYkOYXOBGgfa4r2A/66vYgkSRrfRJO/Z1fVX1bV\npubxceBZXYxL0hZWr17NunXrOPTQQzn22GM59NBDWbduHatXr247NKmfnU5nVMyjAFV1D/DsViOS\nJGkcE03+fpTktUlmN4/XAv/czcAkPdm+++7L0NDQE0M9N2zYwNDQEPvuu2/LkUl9bWNV/dvITpJd\n6NzXLknStDPR5O9NwKuBHwIPAL8DvKFLMUkaw09+8hPWr1/P0NAQV199NUNDQ6xfv56f/OQnbYcm\n9bMbkpwD7J7k5cCn6dwasVVJLkvyUJLvjip7X5L7k9zWPI4fdezdSdY0S0sc15WWSJJmvAlN+FJV\n9wFPmrY6yduBP+lGUJKe6uGHH+bEE0/knHPOYePGjcyZM4cTTjiBlStXth2a1M/OBhYDd9CZwfoa\n4C8mUO/jwJ8Bn9ii/KNV9ZHRBUnmAyfTWXd3X+CrSZ7nrNiSpO010Z6/sbxj0qKQNCFf//rXmTt3\nLkmYO3cuX//619sOSeprVfUzYDnwAeD9wPKq2uawz6r6OvDwBN/mJOCKqtpYVfcCa4CjdjBkSVIf\n25nkL5MWhaRtmj17No8++ihDQ0Ncc801DA0N8eijjzJ7trPKS21JcgLwD8Cf0unJW5PkN3bilENJ\nvtMMC927KdsP+MGo16xryiRJ2i6ZwAXKsSsm36+qAyc5nu2yYMGCuvnmm9sMQZoySXja057GPvvs\nw3333ce3r/kbAAAXpElEQVRznvMcHn74YR599FF29P+x1EuS3FJVC9qOY7Qk3wP+c1WtafZ/Ebi6\nql4wgbrzgC9W1aHN/gDwIzoTxnwAmFtVb0ryZ8DfV9X/aV63DPhSVX1mjHOeCpwKMDAw8KIrrrhi\n5xs5TTz08I958F/bjmLyHLbf08c9tn79evbcc88pjKY9tnVm6qe2QvvtXbhw4YS/H7d6z1+Sxxh7\n1rIAu+9AbJJ2wnOf+1y+/e1vU1Xcd999HHnkkdx6661thyX1s8dGEr/GPwKP7ciJqurBke0klwJf\nbHbvBw4Y9dL9m7KxzrEUWAqdC6SDg4M7Esq0dOEnr+L8OyY0VUFPWPuawXGPDQ8PM5M+u62xrTNT\nP7UVequ9W/0tWlV7TVUgkrZujz324NZbb+Utb3kLxx9/PNdccw0XX3wxe+yxR9uhSX0nyW81mzcn\nuQa4ks7F0v8CfGsHzzm3qh5odl8FjMwEuhK4PMkFdCZ8ORi4aUdjlyT1r5lzCU2a4TZu3Mgee+zB\nl770JT72sY9x4IEHsscee7Bx48a2Q5P60W+O2n4Q+H+b7X9iAiNjkqwABoFnJlkHvBcYTHIEnSRy\nLZ3ZQ6mqO5NcCawCNgGnO9OnJGlHmPxJPWLTpk0sXbqU888/H+j0BL7nPe/hTW96U8uRSf2nqt64\nk/UXjVG8bCuvXwIs2Zn3lCTJ5E/qEXPmzOG8887jnnvuoapYtWoV5513HnPmzGk7NKlvJTkIGALm\nMeo7tapOHK+OJEltMfmTesSzn/1s7r77bpJ/X2Xl7rvv5oADDthKLUld9td0euy+APys5VgkSdoq\nkz+pR/zgB51lvkaWdRh5HimX1IrHq+pP2w5CkqSJMPmTesjee+/NZz/7WTZv3szs2bP57d/+bR55\n5JG2w5L62f9O8l7gK8ATsy9VlWuwSJKmnVltByBp4o4++mgWLlzILrvswsKFCzn66KPbDknqd4cB\npwDnAec3j4+0GpEkSeOw50/qIddccw1vfetbOf7443nrW9/KNddc03ZIUr/7L8AvVNW/tR2IJEnb\nYvIn9Yh99tmHhx9+mIsvvpiLL774SeWSWvNd4BnAQ20HIknStjjsU+oRCxYsAGDWrFlPeh4pl9SK\nZwDfS/I3SVaOPNoOSpKksdjzJ/WIG264gec973ncc889QGe2z+c973nccMMNLUcm9bX3th2AJEkT\nZfIn9YiNGzdy9913M3v2bDZv3sysWbO4++672w5L6mtV5dUXSVLPMPmTeszmzZuf9CypPUkeA6rZ\n/TlgV2BDVT2tvagkSRqbyZ/UY/bee28+9KEP8a53vcs1/qSWVdVeI9tJApwEvLi9iKafeWdfPSnn\nOfOwSTmNJPU1J3yRekgSHnnkEU455RQeeeQROn9rSpoOquOvgePajkWSpLGY/Ek9pKo48cQT+fzn\nP8+JJ55IVW27kqSuSfJbox6/k+Q84PG245IkaSwO+5R6zNVXX83KlSuZPXt226FIgt8ctb0JWEtn\n6KckSdOOyZ/UY5zwRZo+quqNbccgSdJEmfxJPWJkiYexyiVNrSTv2crhqqoPTFkwkiRNkMmf1CNG\nEr8kVNUTz/YASq3YMEbZHsBi4OcBkz9J0rRj8if1mFmzZj2xyLuJn9SOqjp/ZDvJXsAZwBuBK4Dz\nx6snSVKbnO1T6iGHH344L3jBC5g1axYveMELOPzww9sOSepbSfZJ8ofAd+hcTP3lqjqrqh5qOTRJ\nksZk8if1kNtvv52XvvSlXHXVVbz0pS/l9ttvbzskqS8l+TDwLeAx4LCqel9VPdJyWJIkbZXDPqUe\ns3TpUi6++GInepHadSawEfgD4NwkI+WhM+HL09oKTJKk8Zj8ST1k1B+YT+y70Ls09arKkTOSpJ5j\n8ie1ZMtEbiJGz+45erKX7TmXyaIkSVJ/8sql1JKq2q7H5ZdfzkEHHcTXvvY1Dvz9v+ZrX/saBx10\nEJdffvl2nUeSJEn9yZ4/qUcsWrQIgKGhIb6/ajVDXzqEJUuWPFEuSZIkbY3Jn9RDFi1axKJFi5h3\n9tV897wT2g5HkiRJPcRhn5IkSZLUB0z+JEmSJKkPmPxJkiRJUh/oWvKX5LIkDyX57qiyfZJcm+Se\n5nnvUcfenWRNkruSHNetuCRJkiSpH3Wz5+/jwCu2KDsbuK6qDgaua/ZJMh84GXhhU+eiJLO7GJsk\nSZIk9ZWuJX9V9XXg4S2KTwKWN9vLgVeOKr+iqjZW1b3AGuCobsUmSZIkSf1mqu/5G6iqB5rtHwID\nzfZ+wA9GvW5dUyZJkiRJmgStrfNXVZWktrdeklOBUwEGBgYYHh6e7NCknuDPviRJkrbHVCd/DyaZ\nW1UPJJkLPNSU3w8cMOp1+zdlT1FVS4GlAAsWLKjBwcEuhitNU1++Gn/2JUmStD2metjnSuD1zfbr\ngatGlZ+cZE6Sg4CDgZumODZJkqaEM2JLktrQzaUeVgDfAJ6fZF2SxcB5wMuT3AP8WrNPVd0JXAms\nAr4MnF5Vm7sVmyRJLfs4zogtSZpiXRv2WVWLxjl07DivXwIs6VY8kiRNF1X19STztig+CRhstpcD\nw8BZjJoRG7g3yciM2N+YilglSTPHVA/7lCRJY3NGbElSV7U226ckSRrbTJoR+8zDNk3KeQZ2n7xz\nTQdb+2zWr18/LT67qWBbZ6Z+aiv0VntN/iRJmh5m5IzYbzj76kk5z5mHbeL8O2bOny1rXzM47rHh\n4eG+mdHZts5M/dRW6K32OuxTkqTpwRmxJUldNXMuoUmS1COaGbEHgWcmWQe8l84M2Fc2s2PfB7wa\nOjNiJxmZEXsTzogtSdpBJn+SJE0xZ8SWJLXBYZ+SJEmS1AdM/iRJkiSpD5j8SZIkSVIfMPmTJEmS\npD5g8idJkiRJfcDkT5IkSZL6gMmfJEmSJPUBkz9JkiRJ6gMmf5IkSZLUB3ZpOwCp1x3+/q/w43/9\n6ZS/77yzr56y93r67rty+3t/fcreT5IkSZPP5E/aST/+15+y9rwTpvQ9h4eHGRwcnLL3m8pEU5Ik\nSd3hsE9JkiRJ6gMmf5IkSZLUBxz2KUmSNMW2Npz+zMM28YYJDLef6lsOJPU+e/4kSZIkqQ+Y/EmS\nJElSHzD5kyRJkqQ+YPInSZIkSX3A5E+SJEmS+oCzfUqSpDFtbUZKSVLvMfmTdtJeh5zNYcvPnvo3\nXj51b7XXIQBOKS5JktTLTP6knfTY6vOmfK2l4eFhBgcHp+z9vPovSZLU+7znT5IkSZL6gMmfJEmS\nJPUBkz9JkiRJ6gMmf5IkSZLUB0z+JEmSJKkPmPxJkiRJUh8w+ZMkSZKkPmDyJ0mSJEl9wEXepUnQ\nyiLoX56693z67rtO2XtJkiSpO0z+pJ209rwTpvw95519dSvvK0mSpN7lsE9JkiRJ6gMmf5IkSZLU\nB0z+JEmSJKkPeM+fJEnTSJK1wGPAZmBTVS1Isg/wKWAesBZ4dVU90laMkqTe1ErPX5K1Se5IcluS\nm5uyfZJcm+Se5nnvNmKTJGkaWFhVR1TVgmb/bOC6qjoYuK7ZlyRpu7Q57NMvNkmSJuYkYHmzvRx4\nZYuxSJJ61HS6588vNkmSoICvJrklyalN2UBVPdBs/xAYaCc0SVIva+uev5Evts3Ax6pqKX6xSZIE\ncExV3Z/k2cC1Sb43+mBVVZIaq2KTLJ4KMDAwwPDw8E4FcuZhm3aq/mQa2H16xdNNE23rzn6+08H6\n9etnRDsmwrbOXL3U3raSv2nzxSb1Kn/2pZmpqu5vnh9K8nngKODBJHOr6oEkc4GHxqm7FFgKsGDB\nghocHNypWN5w9tU7VX8ynXnYJs6/oz/mqZtoW9e+ZrD7wXTZ8PAwO/tz2its68zVS+1t5bfodPpi\nk3rSl6/umV8ykiYuyR7ArKp6rNn+deB/ASuB1wPnNc9XtRelJKlXTfk9f0n2SLLXyDadL7bv8u9f\nbOAXmySpPw0ANya5HbgJuLqqvkwn6Xt5knuAX2v2JUnaLm30/A0An08y8v6XV9WXk3wLuDLJYuA+\n4NUtxCZJUmuq6h+Bw8co/2fg2KmPSJI0k0x58ucXmyRJkiRNvem01IMkSZIkqUtM/iRJkiSpD5j8\nSZIkSVIfMPmTJEmSpD5g8idJkiRJfcDkT5IkSZL6gMmfJEmSJPUBkz9JkiRJ6gMmf5IkSZLUB0z+\nJEmSJKkPmPxJkiRJUh8w+ZMkSZKkPmDyJ0mSJEl9YJe2A5AkSdL2m3f21Tt9jrXnnTAJkUjqFfb8\nSZIkSVIfsOdPakmSnav/wR2rV1U79b6SJEnqTfb8SS2pqh1+XH/99TtcV5IkSf3J5E+SJEmS+oDD\nPqUeMtZQUXvzJEmSNBH2/Ek9YnTi9wd/8AdjlkuSJEnjMfmTekxVceyxx9rjJ0mSpO3isE+px9jT\nJ0mSpB1hz5/Ug97+9re3HYIkSZJ6jMmf1IP23HPPtkOQJElSjzH5k3rQH/7hH7YdgiRJknqMyZ/U\nY0Yv8i5JkiRNlMmf1GOScNVVVznxiyRJkraLyZ/UI0b39P3Jn/zJmOWSJEnSeEz+pB5SVU8a9mni\nJ0mSpIlynT9JkqQ+Ne/sqyflPGvPO2FSziOpu+z5kyRJkqQ+YPInSZIkSX3A5E+SJEmS+oD3/EmS\nJGmn7Oi9g2cetok3NHW9b1DqPnv+JEmSJKkPmPxJktQDkrwiyV1J1iQ5u+14JEm9x2GfkiRNc0lm\nA38OvBxYB3wrycqqWtVuZNL0MhlLVzj8VDOZyZ8kSdPfUcCaqvpHgCRXACcBJn+aMSZrzUFpqoz8\nzI6+d3V7TfXFBpM/SZKmv/2AH4zaXwf8SkuxSDOaC99rJktVtR3DDkvyT8B9bcchteCZwI/aDkKa\nYs+pqme1HUQbkvwO8Iqq+m/N/uuAX6mq/77F604FTm12nw/cNaWBdlc//d6zrTOTbZ252m7vhL8f\ne7rnr1//CJCS3FxVC9qOQ9KUuR84YNT+/k3Zk1TVUmDpVAU1lfrp955tnZls68zVS+11tk9Jkqa/\nbwEHJzkoyc8BJwMrW45JktRjerrnT5KkflBVm5L8d+BvgNnAZVV1Z8thSZJ6jMmf1Jtm5LAuSeOr\nqmuAa9qOo0X99HvPts5MtnXm6pn29vSEL5IkSZKkifGeP0mSJEnqAyZ/0k5Isn47XvvKJPNH7X88\nyb1Jbmseb5ukmAaT/OpknEuS2pBkbZI7mt+NNzdl+yS5Nsk9zfPebce5I5JcluShJN8dVTZu25K8\nO8maJHclOa6dqHfcOO19X5L7R33/HT/qWE+2N8kBSa5PsirJnUnOaMpn5Ge7lfbOxM92tyQ3Jbm9\naev7m/Ke/GxN/qSp80pg/hZl76yqI5rHn25ZIcnsHXifQcDkT1KvW9j8bhyZPv1s4LqqOhi4rtnv\nRR8HXrFF2Zhtay4Yngy8sKlz0Q5+L7Tp4zy1vQAfHfX9dw30fHs3AWdW1XzgxcDpTXtm6mc7Xnth\n5n22G4GXVdXhwBHAK5K8mB79bE3+pEmWZF6SryX5TpLrkhzY9MSdCHy4uRL2i1upvz7J+UluB45O\ncmySbzdXwS9LMqd53dok709ya3PsBUnmAacB/6N5n/+U5DeTfLM5x1eTDDT1n9VcqbozyV8kuS/J\nM5tjr22uct2W5GPT6ZeWpL51ErC82V5O54Jaz6mqrwMPb1E8XttOAq6oqo1VdS+wBjhqSgKdJOO0\ndzw9296qeqCqbm22HwNWA/sxQz/brbR3PD3b3uoYGem1a/MoevSzNfmTJt+FwPKq+iXgk8CfVtXf\n0VmTa6Sn7x+a144kg7clOawp2wP4ZnOF6WY6V01/t6oOozND71tGvdePquqXgYuB36+qtcAl/PtV\nt78FbgReXFVHAlcA72rqvhf4WlW9EPgMcCBAkkOA3wVeUlVHAJuB10zmP5AkbUMBX01yS5JTm7KB\nqnqg2f4hMNBOaF0xXtv2A34w6nXr2Pof2L1kqLlIetmo4XIzor3NhdgjgW/SB5/tFu2FGfjZJpmd\n5DbgIeDaqurZz9bkT5p8RwOXN9t/BRyzldeOHvZ5R1O2Gfhss/184N6qurvZXw68dFT9zzXPtwDz\nxnmP/YG/SXIH8E46wxBo4roCoKq+DDzSlB8LvAj4VvOL7ljgF7bSBkmabMc0F59+g85wstG/96jO\nVOUzcrrymdy2US6m871yBPAAcH674UyeJHvS+Q5/e1U9OvrYTPxsx2jvjPxsq2pz8ztpf+CoJIdu\ncbxnPluTP2n6ebyqNk/wtRub582Mv27nhcCfNT2HbwZ228Y5Q6fnciQpfX5VvW+C8UjSTquq+5vn\nh4DP0xky9WCSuQDN80PtRTjpxmvb/cABo163f1PW06rqweaP6Z8Bl/LvQ+J6ur1JdqWTCH2yqkYu\nzs7Yz3as9s7Uz3ZEVf0LcD2de/l68rM1+ZMm39/RudEXOsMl/7bZfgzYazvPdRcwL8lzm/3XATds\no86W7/N0/v2XzutHlf9/wKsBkvw6MDI04zrgd5I8uzm2T5LnbGfckrRDkuyRZK+RbeDXge/SGTo/\n8jvs9cBV7UTYFeO1bSVwcpI5SQ4CDgZuaiG+STXyB3PjVXQ+X+jh9iYJsAxYXVUXjDo0Iz/b8do7\nQz/bZyV5RrO9O/By4Hv06Gc7Xk+BpIn5D0nWjdq/ABgC/jLJO4F/At7YHLsCuDSdJR1+ZyInr6rH\nk7wR+HSSXYBv0bmnb2u+AHwmyUlNLO9r6j8CfA04qHnd+4EVSV4HfIPOePXHqupHSf4A+EqSWcBP\ngdOB+yYSsyTtpAHg852/LdkFuLyqvpzkW8CVSRbT+X306hZj3GFJVtCZlfmZzffHe4HzGKNtVXVn\nkiuBVXRmVzx9O0aGTAvjtHcwyRF0hsmtpTMqpdfb+xI6F2jvaG6ZADiHmfvZjtfeRTPws50LLG8m\nv5sFXFlVX0zyDXrws01niKqkftPMGrq5qjYlORq4uBnPLkmSpBnInj+pfx1I54rVLODfgFNajkeS\nJEldZM+fJEmSJPUBJ3yRJEmSpD5g8idJkiRJfcDkT5IkSZL6gMmfJEmSJl2S9dvx2lcmmT9q/+NJ\n7k1yW/N42yTFNJjkVyfjXFIvcrZPSZIkte2VwBfprI024p1V9ZnxKiSZvQPrpw0C64G/2+4IpRnA\nnj9JkiRNiSTzknwtyXeSXJfkwKYn7kTgw00v3y9upf76JOcnuR04OsmxSb6d5I4klzVr2JJkbZL3\nJ7m1OfaCJPOA04D/0bzPf0rym0m+2Zzjq0kGmvrPSnJtkjuT/EWS+5I8szn22iQ3Nef4WLP4t9QT\nTP4kSZI0VS4EllfVLwGfBP60qv4OWEmnp++IqvqH5rUjyeBtSQ5ryvYAvllVhwM3Ax8HfreqDqMz\nou0to97rR1X1y8DFwO9X1VrgEuCjzfv8LXAj8OKqOhK4AnhXU/e9wNeq6oXAZ+isjUuSQ4DfBV5S\nVUcAm4HXTOY/kNRNDvuUJEnSVDka+K1m+6+AD23ltWMN+9wMfLbZfj5wb1Xd3ewvB04H/qTZ/1zz\nfMuo99zS/sCnkswFfg64tyk/BngVQFV9OckjTfmxwIuAbyUB2B14aCttkKYVkz9JkiT1ise34z6/\njc3zZsb/m/dC4IKqWplkEHjfNs4ZOj2X755gDNK04rBPSZIkTZW/A05utl8D/G2z/Riw13ae6y5g\nXpLnNvuvg//bzt2qVBBFYRh+P6NFNAgWvQ5v4HRBrXKCUQTBZvEqvAXxRJtiMBgtKl6BgsFmEVG3\n4WxhFPwDZY7M+8DAsGcPsyZ+bNbi+It33n9nAriu9yuN9RNgGSBJD5is60fAYpLp+mwqydwP65Za\nY/iTJEnSXxhPctW4NoA1oJ/kjGFYW697d4HNOnjlw4EvTaWUe6APDJKcA88Me/o+sw8svA58YXjS\nN0hyCtw29m0DvSQXwBJwA9yVUi6BLeCg/sMhMPOdeqVRkFJK2zVIkiRJI6NODX0qpTwmmQd26oAX\n6V+z50+SJEl6axbYSzIGPACrLdcj/QpP/iRJkiSpA+z5kyRJkqQOMPxJkiRJUgcY/iRJkiSpAwx/\nkiRJktQBhj9JkiRJ6gDDnyRJkiR1wAs9VYvA9FRHvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461d4bbda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5IAAAF3CAYAAADXZ3QKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACALUISAACALUISAACALedm6IfIKy6h/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461dc039e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461dbfaba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKlXgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461da954a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiIqKL/Ru72uE+cu1U3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461db535f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAE+VsPclkkhQAAAGMBE6rgMkna9gMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461db53780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461d953be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X2YXWV56P/vTcJLDoKGYkcIaLAn1QlwiJCiB6hOjJY3\nC2hbJaJiSYlW4OBpzjGBtCrKXBA9wR5RtKHJRTg/DFBfEpQXQcyWE1sUEDSQkRohHBMDKLTkpRIy\n4f79sdeEnTCT2ZPZM2v27O/nuva113rWWs+693PtzM69nmc9KzITSZIkSVJr2qvsACRJkiRJ5TEp\nlCRJkqQWZlIoSZIkSS3MpFCSJEmSWphJoSRJkiS1MJNCSZIkSWphJoWSJEmS1MJMCiVJkiSphZkU\nSpIkSVILMymUJEmSpBY2tuwAhsrBBx+cEydOLDsMaVht2bKF/fffv+wwpGH1wAMP/DYzX112HM2i\nUb+P/r2pj+1UH9upPrZTfWynl9T7Gzlqk8KJEydy//33lx2GNKwqlQodHR1lhyENq4h4ouwYmkmj\nfh/9e1Mf26k+tlN9bKf62E4vqfc30uGjkiRJktTCTAolSZIkqYWZFEqSJElSCzMplCRJkqQWZlIo\nSZIkSS3MpFCSJEmSWphJoSRJkiS1MJNCSZIkSWphJoWSJEmS1MJMCqVRYOnSpRx11FFMnz6do446\niqVLl5YdkiRJkprE2LIDkDQ4S5cuZd68eSxatIjt27czZswYZs6cCcCMGTNKjk6SJEkjnT2FUpPr\n7Oxk0aJFTJs2jbFjxzJt2jQWLVpEZ2dn2aFJkiSpCZgUSk2uq6uLk046aaeyk046ia6urpIikiRJ\nUjNx+KjU5Nrb21m5ciXTpk3bUbZy5Ura29tLjErSaLdq/XN8eO6tg6pj7ZWnNygaSdJg2FMoNbl5\n8+Yxc+ZMVqxYQXd3NytWrGDmzJnMmzev7NAkSZLUBOwplJpcz2QyF110EV1dXbS3t9PZ2ekkM5Ik\nSaqLSaE0CsyYMYMZM2ZQqVTo6OgoOxxJkiQ1EYePSpIkSVILMymUJEmSpBZmUihJkiRJLcykUJIk\nSZJamEmhJEmSJLUwk0JJkiRJamEmhZIkSZLUwkwKJUmSJKmFmRRKkiRJUgszKZQkSZKkFmZSKEmS\nJEktzKRQkqQRIiIOj4gVEbE6Ih6JiIuL8oMi4q6I+EXxPr7mmEsiYk1EPBoRJ5cXvSSpWZkUSpI0\ncnQDszNzMvAW4IKImAzMBe7OzEnA3cU6xbazgSOBU4BrImJMKZFLkpqWSaEkSSNEZm7IzJ8Uy5uA\nLmACcCawpNhtCXBWsXwmcGNmbs3Mx4E1wPHDG7UkqdmZFEqSNAJFxETgTcCPgLbM3FBsehJoK5Yn\nAL+qOWxdUSZJUt3Glh2AJEnaWUS8AvgG8PHM3BgRO7ZlZkZEDrC+WcAsgLa2NiqVyqBjbBsHs4/u\nHlQdjYhjpNu8eXNLfM7Bsp3qYzvVx3YaOJNCaRRYunQpnZ2ddHV10d7ezrx585gxY0bZYUnaAxGx\nN9WE8IbM/GZR/FREHJKZGyLiEODponw9cHjN4YcVZTvJzIXAQoCpU6dmR0fHoOO8+oblLFg1uP9G\nrD1n8HGMdJVKhUa092hnO9XHdqqP7TRwJoVSk1u6dCnz5s1j0aJFbN++nTFjxjBz5kwAE0OpyUS1\nS3AR0JWZV9VsugU4F7iyeF9eU/61iLgKOBSYBPx4+CKWJI0G3lMoNbnOzk4WLVrEtGnTGDt2LNOm\nTWPRokV0dnaWHZqkgTsR+CDw9oh4qHidRjUZfGdE/AJ4R7FOZj4C3AysBu4ALsjM7eWELklqVvYU\nSk2uq6uLk046aaeyk046ia6urpIikrSnMnMlEH1snt7HMZ2AV4EkSXvMpFBqcu3t7Vx22WUsW7Zs\nxz2FZ511Fu3t7WWHJkmSpCZgUig1uWnTpjF//nzmz5/P5MmTWb16NXPmzOGjH/1o2aFJkiSpCZgU\nSk1uxYoVzJkzh8WLF+/oKZwzZw7Lli0rOzRJkiQ1AZNCqcl1dXXx4IMPcvnll++Ygnnbtm1cccUV\nZYcmSZKkJuDso1KTa29vZ+XKlTuVrVy50nsKJUmSVBeTQqnJzZs3j5kzZ7JixQq6u7tZsWIFM2fO\nZN68eWWHJkmSpCbg8FGpyfU8oP6iiy7acU9hZ2enD66XJElSXUwKpVFgxowZzJgxY8c9hZIkSVK9\nHD4qSZIkSS3MpFAaBZYuXcpRRx3F9OnTOeqoo1i6dGnZIUmSJKlJmBRKTW7p0qVcfPHFbNmyhcxk\ny5YtXHzxxSaGkiRJqotJodTkPvGJTzBmzBgWL17MnXfeyeLFixkzZgyf+MQnyg5NkiRJTcCkUGpy\n69at4/rrr2fatGmMHTuWadOmcf3117Nu3bqyQ5MkSVITMCmUJEmSpBY2ZElhRBweESsiYnVEPBIR\nFxflB0XEXRHxi+J9fM0xl0TEmoh4NCJOrik/LiJWFdu+GBExVHFLzeawww7j3HPP3enh9eeeey6H\nHXZY2aFJkiSpCQxlT2E3MDszJwNvAS6IiMnAXODuzJwE3F2sU2w7GzgSOAW4JiLGFHV9BTgfmFS8\nThnCuKWm8rnPfY7Nmzdz8skn8853vpOTTz6ZzZs387nPfa7s0CRJktQEhiwpzMwNmfmTYnkT0AVM\nAM4ElhS7LQHOKpbPBG7MzK2Z+TiwBjg+Ig4BDszMezMzgetrjpEE7LfffkyYMIGIYMKECey3335l\nhyRJkqQmMSz3FEbEROBNwI+AtszcUGx6EmgrlicAv6o5bF1RNqFY3rVcEtDZ2clNN93E448/zve/\n/30ef/xxbrrpJjo7O8sOTZIkSU1g7FCfICJeAXwD+Hhmbqy9HTAzMyKygeeaBcwCaGtro1KpNKpq\nacTq6upi+/btVCoVNm/eTKVSYfv27XR1dflvQJIkSf0a0qQwIvammhDekJnfLIqfiohDMnNDMTT0\n6aJ8PXB4zeGHFWXri+Vdy18mMxcCCwGmTp2aHR0djfoo0ojV3t5OpVJh2bJldHV10d7ezllnnUV7\nezv+G5AkSVJ/hnL20QAWAV2ZeVXNpluAc4vlc4HlNeVnR8S+EXEE1QllflwMNd0YEW8p6vxQzTFS\ny5s2bRrz58/nvPPO49Zbb+W8885j/vz5TJs2rezQJEmS1ASGsqfwROCDwKqIeKgouxS4Erg5ImYC\nTwDvBcjMRyLiZmA11ZlLL8jM7cVxHwOuA8YBtxcvScCKFSuYM2cOixcv3tFTOGfOHJYtW1Z2aJIk\nSWoCQ5YUZuZKoK/nCU7v45hO4GWzY2Tm/cBRjYtOGj26urp48MEHufzyy6lUKnR0dLBt2zauuOKK\nskOTJElSExjyiWYkDa329nYuu+yyXu8plCRJkvpjUig1uZ57CufPn8/kyZNZvXo1c+bM4aMf/WjZ\noUmSJKkJmBRKTW7FihW8613v4tJLL2Xr1q3su+++vOtd72LFihVlhyZJkqQmYFIoNbnVq1fzH//x\nH9x+++1s376dMWPGMHPmTNauXVt2aJI05CbOvXXQday98vQGRCJJzcukUGpy++yzD4ceeiinnnrq\njp7CqVOn8utf/7rs0CRJktQEhuw5hZKGx9atW/nhD3/Ieeedx7e//W3OO+88fvjDH7J169ayQ5M0\nQBGxOCKejoiHa8puioiHitfansc8RcTEiPhdzbavlhe5JKmZmRRKTS4imD59Ovfccw9nnnkm99xz\nD9OnTyeiryfCSBrBrgNOqS3IzPdl5pTMnAJ8A/hmzeZf9mzLTGeXkiTtEYePSk0uM/nlL3/J4sWL\nd9xTeN5555GZZYcmaYAy856ImNjbtqhe6Xkv8PbhjEmSNPqZFEpNbt999+XEE0/koosu2vGcwhNP\nPJENGzaUHZqkxvpj4KnM/EVN2RHFcNLngL/NzP9bTmiSpGZmUig1ufPPP59rrrmGV7/61WQmv/3t\nb1m6dCkf+9jHyg5NUmPNAJbWrG8AXpuZz0TEccCyiDgyMzfuemBEzAJmAbS1tVGpVAYdTNs4mH10\n96DqaEQcg42hUXH0ZfPmzUNa/2hhO9XHdqqP7TRwJoVSkzvhhBNYsmQJzz77LJnJs88+y/77788J\nJ5xQdmiSGiQixgLvAY7rKcvMrcDWYvmBiPgl8IfA/bsen5kLgYUAU6dOzY6OjkHHdPUNy1mwanD/\njVh7zuDj+HAjHknRgDj6UqlUaER7j3a2U31sp/rYTgPnRDNSk+vs7GT58uW88MILrFixghdeeIHl\ny5fT2dlZdmiSGucdwM8zc11PQUS8OiLGFMuvByYBj5UUnySpiZkUSk2uq6uLk046aaeyk046ia6u\nrpIikrSnImIp8C/AGyJiXUTMLDadzc5DRwHeCvysuKfw68BHM/PZ4YtWkjRaOHxUanLt7e1cdtll\nLFu2bMdEM2eddRbt7e1lhyZpgDJzRh/lH+6l7BtUH1EhSdKgmBRKTW7atGnMnz+f+fPnM3nyZFav\nXs2cOXP46Ed9ZJkkSZL6Z1IoNbkVK1YwZ84cFi9evKOncM6cOSxbtqzs0CRJktQETAqlJtfV1cWD\nDz7I5ZdfvmO2rW3btnHFFVeUHZokSZKagBPNSE2uvb2dlStX7lS2cuVK7ymUJElSXewplJrcvHnz\nOO2003j++ed3lO23334sXry4xKgkSZLULOwplJrcddddx/PPP8/48eMBGD9+PM8//zzXXXdduYFJ\nkiSpKdhTKDW5u+66i+nTp/Pkk0/y3HPPceihh3Lsscdy1113lR2aJDWFiXNvbUg9a688vSH1SNJw\ns6dQanKZyYMPPsiWLVsA2LJlCw8++CCZWXJkkiRJagYmhdIosGnTJoAdiWDPuiRJktQfh49Ko8C2\nbdt44oknyMwd75IkSVI97CmURomeRNCEUJIkSQNhUiiNAhHBggULuP3221mwYAERUXZIkiRJahIO\nH5VGgbFjxzJ37ly2bdvG3nvvzdixY9m2bVvZYUmSJKkJ2FMojQLbtm3joIMOIiI46KCDTAglSZJU\nN3sKpSa31157vex+wohwCKmkEa9RzweUJA2OPYVSk8tMxo4dy1NPPQXAU089xdixY51wRpIkSXUx\nKZSa3Pjx4+nu7uY1r3kNe+21F695zWvo7u5m/PjxZYcmSZKkJmBSKDW5jRs3ss8++/DMM8/w4osv\n8swzz7DPPvuwcePGskOTJElSE/CeQqnJdXd3093dvWPdSWYkSZI0EPYUSqOAzymUJEnSnjIplCRJ\nkqQW5vBRaRR485vfzKWXXsrWrVvZd999efOb38y9995bdliSJElqAiaF0gg00OGftQng1q1bd6wP\npB4fYSFJktSaHD4qjUCZWffrwgsvJCIYM2YMAGPGjCEiuPDCCwdUjyRJklqTPYVSk7v66qsBuPba\na9m+fTtjx47l/PPP31EuSZIk7Y49hdIocPXVV/P888/zujnf4fnnnzchlCRJUt1MCiVJkiSphZkU\nSpI0QkTE4oh4OiIerin7dESsj4iHitdpNdsuiYg1EfFoRJxcTtSSpGZnUihJ0shxHXBKL+VfyMwp\nxes2gIiYDJwNHFkcc01EjBm2SCVJo4ZJoSRJI0Rm3gM8W+fuZwI3ZubWzHwcWAMcP2TBSZJGLZNC\nSZJGvosi4mfF8NLxRdkE4Fc1+6wryiRJGhAfSSFJUoNFxInAQ5m5JSI+ABwL/O/MfGIPqvsK8Fkg\ni/cFwHkDjGcWMAugra2NSqWyB2HsrG0czD66e9D1jCa9tevmzZsb0t6jne1UH9upPrbTwJkUSpLU\neF8BjomIY4DZwD8C1wNvG2hFmflUz3JEXAt8p1hdDxxes+thRVlvdSwEFgJMnTo1Ozo6BhrGy1x9\nw3IWrPK/EbXWntPxsrJKpUIj2nu0s53qYzvVx3YaOIePSpLUeN2ZmVTv+/tSZn4ZOGBPKoqIQ2pW\n3w30zEx6C3B2ROwbEUcAk4AfDyJmSVKL8hKfJEmNtykiLgE+ALw1IvYC9u7voIhYCnQAB0fEOuBT\nQEdETKE6fHQt8BGAzHwkIm4GVgPdwAWZuX0IPoskaZQzKZQkqfHeB7wfmJmZT0bEa4HP93dQZs7o\npXjRbvbvBDr3OEpJkjAplCSpoYpnBS7NzGk9ZZn5/6jeUyhJ0ojjPYWSJDVQMYTzxYh4ZdmxSJJU\nD3sKJUlqvM3Aqoi4C9jSU5iZ/628kCRJ6p1JoSRJjffN4iVJ0ohnUihJUoNl5pKIGAe8NjMfLTse\nSZJ2Z8juKYyIxRHxdEQ8XFP26YhYHxEPFa/TarZdEhFrIuLRiDi5pvy4iFhVbPtiRMRQxSxJUiNE\nxJ8CDwF3FOtTIuKWcqOSJKl3QznRzHXAKb2UfyEzpxSv2wAiYjJwNnBkccw1xextAF8Bzqf6UN5J\nfdQpSdJI8mngeODfATLzIeD1ZQYkSVJfhiwpzMx7gGfr3P1M4MbM3JqZjwNrgOMj4hDgwMy8NzOT\n6nTeZw1NxJIkNcy2zHxul7IXS4lEkqR+lPFIiosi4mfF8NLxRdkE4Fc1+6wryiYUy7uWS5I0kj0S\nEe8HxkTEpIi4GvjnsoOSJKk3wz3RzFeAzwJZvC8AzmtU5RExC5gF0NbWRqVSaVTVUtPwey+NCBcB\n84CtwFLgu1R/9yRJGnGGNSnMzKd6liPiWuA7xep64PCaXQ8rytYXy7uW91X/QmAhwNSpU7Ojo6Mh\ncUtN445b8XsvlS8z/4NqUjiv7FgkSerPsCaFEXFIZm4oVt8N9MxMegvwtYi4CjiU6oQyP87M7RGx\nMSLeAvwI+BBw9XDGLEnSQEXEt6mOiqn1HHA/8A+Z+fzwRyVJUu+GLCmMiKVAB3BwRKwDPgV0RMQU\nqj+Ua4GPAGTmIxFxM7Aa6AYuyMztRVUfozqT6Tjg9uIlSdJI9hjwaqpDRwHeB2wC/hC4FvhgSXFJ\nkvQyQ5YUZuaMXooX7Wb/TqCzl/L7gaMaGJokSUPthMz8o5r1b0fEfZn5RxHxSGlRSZLUizJmH5Uk\nabR7RUS8tmelWH5FsfpCOSFJktS74Z59VJKkVjAbWBkRvwQCOAL4WETsDywpNTJJknZhUihJUoNl\n5m0RMQl4Y1H0aM3kMn9fUliSJPXKpFCSpKFxHDCR6m/tMRFBZl5fbkiSJL2cSaEkSQ0WEf8H+APg\nIaBnNu0ETAolSSOOSaEkSY03FZicmbs+q1CSpBHHpFCSpMZ7GHgNsKHsQNRcJs69tewQdlh75ell\nhyBpmJgUSpLUeAcDqyPix8DWnsLMPKO8kCRJ6p1JoSRJjffpsgOQJKleJoWSJDVYZv4gIl4HTMrM\n70XEfwLGlB2XJEm92avsACRJGm0i4nzg68A/FEUTgGXlRSRJUt92mxRGxHtqlscPfTiSJI0KFwAn\nAhsBMvMXwO+XGpEkSX3or6fwb2uW7x7KQCRJGkW2ZuYLPSsRMZbqcwolSRpx+ksKo49lSZLUtx9E\nxKXAuIh4J/BPwLdLjkmSpF71lxSOi4g3RcRxwH7F8rE9r+EIUJKkJjQX+A2wCvgIcBs7j77pVUQs\njoinI+LhmrLPR8TPI+JnEfGtiHhVUT4xIn4XEQ8Vr68O0WeRJI1y/c0++iRwVS/LUB0G8/ahCEqS\npGaWmS8C1wLXRsRBwGGZWc/w0euALwHX15TdBVySmd0RMR+4BJhTbPtlZk5pXOSSpFa026QwMzuG\nKQ5JkkaNiKgAZ1D9nX0AeDoi/jkz//vujsvMeyJi4i5ld9as3gv8eUODlSS1vP5mH/2jiHhNzfqH\nImJ5RHyxuPIpSZJe7pWZuRF4D3B9Zr4ZmN6Aes8Dbq9ZP6IYOvqDiPjjBtQvSWpB/Q0f/QfgHQAR\n8VbgSuAiYAqwEK9WSpLUm7ERcQjwXmBeIyqMiHlAN3BDUbQBeG1mPlPc+78sIo4sktFdj50FzAJo\na2ujUqkMOp62cTD76O5B1zOa9NaumzdvHlB7j6Q2bcT3pF4DbadWZTvVx3YauP6SwjGZ+Wyx/D5g\nYWZ+A/hGRDw0tKFJktS0PgN8F1iZmfdFxOuBX+xpZRHxYeBdwPSeexMzcyuwtVh+ICJ+CfwhcP+u\nx2fmQqoXc5k6dWp2dHTsaSg7XH3Dchas6u+/Ea1l7TkdLyurVCoMpL0/PPfWxgU0SL19nqEy0HZq\nVbZTfWynges3KYyIsZnZTXXYy6wBHCtJUkvKzH+i+hiKnvXHgD/bk7oi4hTgE8DbMvM/aspfDTyb\nmduLpHMS8NigAtegTOwloZt9dPeISvQkqTf9PZJiKdVnLS0Hfgf8X4CI+M/Ac0McmyRJTSkiPhcR\nB0bE3hFxd0T8JiI+UMdxS4F/Ad4QEesiYibV2UgPAO7a5dETbwV+Vozc+Trw0ZrRPZIk1a2/3r6v\nAXcDhwB31kynvRfVewslSdLL/UlmfiIi3g2spTrhzD3A/7e7gzJzRi/Fi/rY9xvANwYZpyRJ/SaF\nX8/M4yLi7sz8Vk9hZv7rEMclSVIz6/l9PR34p8x8LiLKjEeSpD71lxTuFRGXAn8YEX+z68bMvKqX\nYyRJanXfiYifU7314q+L+/+eLzkmSZJ61d89hWcD26kmjwf08pIkSbvIzLnACcDUzNwGbAHOLDcq\nSZJ6t9uewsx8FJgfET/LzNt3t68kSdrJocA7ImK/mrLrywpGkqS+1PtYie9HxPuBibXHZOZnhiIo\nSZKaWUR8CugAJgO3AacCKzEplCSNQP0NH+2xnOqwl26qQ2B6XpIk6eX+nOrzfZ/MzL8EjgFeWW5I\nkiT1rt6ewsMy85QhjUSSpNHjd5n5YkR0R8SBwNPA4WUHJUlSb+rtKfzniDh6SCORJGn0uD8iXgVc\nCzwA/ITqQ+klSRpx6u0pPAn4cEQ8DmwFAsjM/C9DFpkkSU0qMz9WLH41Iu4ADszMn5UZkyRJfak3\nKTx1SKOQJGmUiYj3UL2omlQnmTEplCSNSLtNCiPioGJxU/GewL9nZg5pVJIkNbGIuAb4z8DSougj\nEfGOzLygxLAkSepVfz2FD1BNBKOm7BUR8VPgrzJz7VAFJklSE3s70N5zETUilgCPlBuSJEm96+/h\n9Uf0Vl4Mifkq4IykkiS93BrgtcATxfrhRZkkSSNOvbOP7iQzvwn8foNjkSRptDgA6IqISkSsAFYD\nB0bELRFxS8mxSZK0k3onmtlJRLyCPUwoJUlqAZ8sOwBJkurV30Qzf9NL8XjgDOBLQxKRJElNLjN/\nUHYMkiTVq7+ewgN2WU/gSeADmblqaEKSJEmSJA2X/iaauQwgIv4iM/+pdltvZZIkSZKk5lLvfYGX\n1FkmSVLLioi7i/f5ZcciSVK9+run8FTgNGBCRHyxZtOBQPdQBiZJUhM6JCJOAM6IiBvZ+Tm/ZOZP\nyglLkqS+9XdP4a+B+6lOLPNATfkm4L8PVVCSJDWpTwJ/BxwGXLXLtqT6UHtJkkaU/u4p/Cnw04j4\nWmZuG6aYJElqSpn5deDrEfF3mfnZsuORJKke9T6n8OSI+CzwuuKYADIzDxyyyCRJalKZ+dmIOAN4\na1FUyczvlBmTJEl9qTcp/HvgPcCqzMwhjEeSpKYXEVcAxwM3FEUXR8QJmXlpiWFJktSrepPCXwEP\nmxBKklSX04EpmfkiQEQsAR4ETAolSSNOvUnhJ4DbIuIHwNaewszc9SZ6SZJU9Srg2WL5lWUGIknS\n7tSbFHYCm4H9gH2GLhxJkkaFK4AHI2IF1fvw3wrMLTckSZJ6V29SeGhmHjWkkUiSNEpk5tKIqAB/\nVBTNycwn+zsuIhYD7wKe7vndjYiDgJuAicBa4L2Z+W/FtkuAmcB24L9l5ncb+0kkSa1grzr3uy0i\n/mRII5EkaRTJzA2ZeUvx6jchLFwHnLJL2Vzg7sycBNxdrBMRk4GzgSOLY66JiDENCV6S1FLqTQr/\nGrgjIn4XERsjYlNEbBzKwCRJajWZeQ8v3YfY40xgSbG8BDirpvzGzNyamY8Da6jOeCpJ0oDUNXw0\nMw8Y6kAkSVKv2jJzQ7H8JNBWLE8A7q3Zb11RJknSgNSVFEbEicBDmbklIj4AHAv8fWb+v90c05D7\nIiLiOKrDacYBtwEX+2gMSdJIVQzhfCQz39joujMzI2LAv4ERMQuYBdDW1kalUhl0LG3jYPbR3YOu\nZ7Rr5nZqxPekXps3bx7W8zUr26k+ttPA1TvRzFeAYyLiGGA28I/A/wHetptjrgO+BFxfU9ZzX8SV\nETG3WJ+zy30RhwLfi4g/zMztxbnPB35ENSk8Bbi9zrglSRpWmbk9Ih6NiNfu7uLpADwVEYdk5oaI\nOAR4uihfDxxes99hRVlvMS0EFgJMnTo1Ozo6Bh3U1TcsZ8Gqev8b0bpmH93dtO209pyOYTtXpVKh\nEd/L0c52qo/tNHD13lPYXfTOnQl8KTO/DOx2SGkj7osofvwOzMx7i/NfX3OMJEkj1XjgkYi4OyJu\n6XntYV23AOcWy+cCy2vKz46IfSPiCGAS8ONBRS1Jakn1XrraVAzv/ADw1ojYC9h7D8430PsithXL\nu5ZLkjSS/d2eHBQRS4EO4OCIWAd8CrgSuDkiZgJPAO8FyMxHIuJmYDXQDVxQjLCRJGlA6k0K3we8\nH5iZmU9GxGuBzw/mxHt6X8TuDMU9E1Kz8XsvlS8zfxARrwMmZeb3IuI/Af0+LiIzZ/SxaXof+3cC\nnXseqSRJ9c8++iRwFUBEHAz8KjOv3/1RvRrofRHri+Vdy/uKs+H3TEhN5Y5bHUMvjQARcT7Vi5QH\nAX9AdZTLV+kjuZMkqUy7vacwIt4SEZWI+GZEvCkiHgYepprc7fpw3XoM6L6IYqjpxiKOAD5Uc4wk\nSSPVBcCJwEaAzPwF8PulRiRJUh/66yn8EnAp8Erg+8CpmXlvRLwRWArc0deBDbwv4mO89EiK23Hm\nUUnSyLc1M1+oXs+EiBgL+DglSdKI1F9SODYz7wSIiM9k5r0Amfnznh+6vjTqvojMvB84qp84JUka\nSX4QEZcC4yLinVQvcH675JgkSepVf4+keLFm+Xe7bPOKpyRJvZsL/AZYBXyE6nN2/7bUiCRJ6kN/\nPYXHRMSgxrTMAAAVMElEQVRGIKhe7dxYlAew35BGJklSk8rMFyNiCfAjqhdRHy2etytJ0oiz26Qw\nM/udPluSJO0sIk6nOtvoL6leSD0iIj6Smd4XL0kacep9TqEkSarfAmBaZq4BiIg/AG7FydIkSSNQ\nf/cUSpKkgdvUkxAWHgM2lRWMJEm7Y0+hJEkNEhHvKRbvj4jbgJup3lP4F8B9pQUmSdJumBRKktQ4\nf1qz/BTwtmL5N1SftytJ0ohjUihJUoNk5l+WHYMkSQNlUihJUoNFxBHARcBEan5rM/OMsmKSJKkv\nJoWSJDXeMmAR8G3gxZJjkSRpt0wKJUlqvOcz84tlByFJUj1MCiVJarz/HRGfAu4EtvYUZuZPygtJ\nkqTemRRKktR4RwMfBN7OS8NHs1iXJGlEMSmUJKnx/gJ4fWa+UHYgkiT1Z6+yA5AkaRR6GHhV2UFI\nklQPewolSWq8VwE/j4j72PmeQh9JIUkacUwKJUlqvE+VHYA0WBPn3jroOtZeeXoDIpE01EwKJUlq\nsMz8QdkxSJJUL5NCSZIaLCI2UZ1tFGAfYG9gS2YeWF5UkiT1zqRQGiLHXHYnz/1u27CftxHDfer1\nynF789NP/cmwnU9qFpl5QM9yRARwJvCW8iKSJKlvJoXSEHnud9uG/V6KSqVCR0fHsJ1vOBNQqVll\nZgLLiofZzy07HkmSdmVSKElSg0XEe2pW9wKmAs+XFI4kSbtlUihJUuP9ac1yN7CW6hBSSZJGHJNC\nSZIaLDP/spH1RcQbgJtqil4PfJLq8xDPB35TlF+ambc18tySpNHPpFCSpAaJiE/uZnNm5mf3pN7M\nfBSYUpxjDLAe+Bbwl8AXMvN/7Um9kiSBSaEkSY20pZey/YGZwO8Be5QU7mI68MvMfKI6sakkSYNj\nUihJUoNk5oKe5Yg4ALiYam/ejcCCvo4boLOBpTXrF0XEh4D7gdmZ+W8NOo8kqUWYFEqS1EARcRDw\nN8A5wBLg2EYlahGxD3AGcElR9BWqvY9ZvC8AzuvluFnALIC2tjYqlcqgY2kbB7OP7h50PaNdq7dT\nvd+1zZs3N+R7OdrZTvWxnQbOpFCSpAaJiM8D7wEWAkdn5uYGn+JU4CeZ+RRAz3tx7muB7/R2UGYu\nLGJi6tSp2YjnmV59w3IWrPK/Ef2ZfXR3S7fT2nM66tpvuJ+z26xsp/rYTgO3V9kBSJI0iswGDgX+\nFvh1RGwsXpsiYmMD6p9BzdDRiDikZtu7gYcbcA5JUotp3UtXkiQ1WGYO2cXWiNgfeCfwkZriz0XE\nFKrDR9fusk2SpLqYFEqS1AQycwvVGUxryz5YUjiSpFHE4aOSJEmS1MJMCiVJkiSphZkUSpIkSVIL\nMymUJEmSpBZmUihJkiRJLcykUJIkSZJamEmhJEmSJLUwk0JJkiRJamEmhZIkSZLUwkwKJUmSJKmF\nmRRKkiRJUgszKZQkSZKkFmZSKEmSJEktzKRQkiRJklqYSaEkSZIktTCTQkmSJElqYWPLDkCSJEmj\n08S5t9a13+yju/lwH/uuvfL0RoYkqRf2FEqSJElSCzMplCRJkqQWZlIoSZIkSS3MpFCSJEmSWphJ\noSRJkiS1MJNCSZIkSWphpSSFEbE2IlZFxEMRcX9RdlBE3BURvyjex9fsf0lErImIRyPi5DJiliRJ\nkqTRqMyewmmZOSUzpxbrc4G7M3MScHexTkRMBs4GjgROAa6JiDFlBCxJkiRJo81IGj56JrCkWF4C\nnFVTfmNmbs3Mx4E1wPElxCdJkiRJo05ZSWEC34uIByJiVlHWlpkbiuUngbZieQLwq5pj1xVlkiRJ\nkqRBGlvSeU/KzPUR8fvAXRHx89qNmZkRkQOttEgwZwG0tbVRqVQaEqy0p4b7O7h58+ZhP6f/ziRJ\nkppbKUlhZq4v3p+OiG9RHQ76VEQckpkbIuIQ4Oli9/XA4TWHH1aU9VbvQmAhwNSpU7Ojo2OIPoFU\nhztuZbi/g5VKZXjPWcJnlFpVRKwFNgHbge7MnBoRBwE3AROBtcB7M/PfyopRktSchj0pjIj9gb0y\nc1Ox/CfAZ4BbgHOBK4v35cUhtwBfi4irgEOBScCPhztuaaAOaJ/L0UvmDv+Jl/S/S6Mc0A5w+vCd\nUNK0zPxtzXrPJG1XRsTcYn1OOaFJkppVGT2FbcC3IqLn/F/LzDsi4j7g5oiYCTwBvBcgMx+JiJuB\n1UA3cEFmbi8hbmlANnVdydorhzdhGu6ewolzbx22c0nq1ZlAR7G8BKhgUihJGqBhTwoz8zHgmF7K\nnwGm93FMJ9A5xKFJkjSS9UzSth34h+KWib4maZMkqW5lTTQjSZIGZo8naRuKidjaxsHso7sHXc9o\nZzvVZ3ft5IRmLyljQrlmZDsNnEmhJElNYICTtO16bMMnYrv6huUsWOV/I/oz++hu26kOu2unted0\nDG8wI9iwTyjXpGyngRtJD6+XJEm9iIj9I+KAnmWqk7Q9zEuTtMHOk7RJklQ3L11JkjTyDWiSNkmS\nBsKkUJKkEW5PJmmTJKleDh+VJEmSpBZmUihJkiRJLczho5IkSRqxJs69ddB1rL3y9AZEIo1e9hRK\nkiRJUgszKZQkSZKkFmZSKEmSJEktzKRQkiRJklqYSaEkSZIktTCTQkmSJElqYSaFkiRJktTCTAol\nSZIkqYX58HpJkiSpDhPn3jroOtZeeXoDIpEay55CSZIkSWphJoWSJEmS1MJMCiVJkiSphZkUSpIk\nSVILMymUJEmSpBZmUihJkiRJLcykUJIkSZJamEmhJEmSJLUwH14vSZKkUa0RD52XRjN7CiVJkiSp\nhZkUSpIkSVILMymUJEmSpBbmPYWSJEnSMBnM/Y2zj+7mw3NvZe2VpzcwIsmkUBpSpdzYfsfwnfOV\n4/YetnNJkiRpaJgUSkOkjKt4E716KEmSpAHynkJJkka4iDg8IlZExOqIeCQiLi7KPx0R6yPioeJ1\nWtmxSpKajz2FkiSNfN3A7Mz8SUQcADwQEXcV276Qmf+rxNgkSU3OpFCSpBEuMzcAG4rlTRHRBUwo\nNypJ0mjh8FFJkppIREwE3gT8qCi6KCJ+FhGLI2J8aYFJkpqWPYWSJDWJiHgF8A3g45m5MSK+AnwW\nyOJ9AXBeL8fNAmYBtLW1UalUBh1L27jq9PjaPdupPrZTfXraqRH/hkezzZs320YDZFIoSVITiIi9\nqSaEN2TmNwEy86ma7dcC3+nt2MxcCCwEmDp1anZ0dAw6nqtvWM6CVf43oj+zj+62nepgO9Wnp53W\nntNRdigjWqVSoRF/51qJw0clSRrhIiKARUBXZl5VU35IzW7vBh4e7tgkSc3PSzKSJI18JwIfBFZF\nxENF2aXAjIiYQnX46FrgI+WEJ2k4TZx7a0Pq8dnG6mFSKEnSCJeZK4HoZdNtwx2LJGn0cfioJEmS\nJLUwk0JJkiRJamEmhZIkSZLUwkwKJUmSJKmFmRRKkiRJUgszKZQkSZKkFmZSKEmSJEktzKRQkiRJ\nklqYSaEkSZIktTCTQkmSJElqYSaFkiRJktTCxpYdgCRJkqThN3HurYOuY+2VpzcgEpXNnkJJkiRJ\namFNkxRGxCkR8WhErImIuWXHI0mSJEmjQVMkhRExBvgycCowGZgREZPLjUqSJEmSml9TJIXA8cCa\nzHwsM18AbgTOLDkmSZIkSWp6zZIUTgB+VbO+riiTJEmSJA3CqJp9NCJmAbMA2traqFQq5QYk7aFp\n06bt8bExf8+OW7FixR6fU5IktSZnMB0dmiUpXA8cXrN+WFG2k8xcCCwEmDp1anZ0dAxLcFKjZeYe\nHVepVPB7L0mSpIFoluGj9wGTIuKIiNgHOBu4peSYJEmSJKnpNUVPYWZ2R8SFwHeBMcDizHyk5LAk\nSZIkqek1RVIIkJm3AbeVHYckSZIkjSbNMnxUkiRJkjQEmqanUJIkSdLo04gZTMFZTAfDnkJJkiRJ\namEmhZIkSZLUwkwKJUmSJKmFeU+hJEmSpKbXc2/i7KO7+fAe3qfYqvcl2lMoSVITi4hTIuLRiFgT\nEXPLjkeS1HzsKZQkqUlFxBjgy8A7gXXAfRFxS2auLjcySWpOjZgJtRl7G00KJUlqXscDazLzMYCI\nuBE4EzAplKSSNGNi6fBRSZKa1wTgVzXr64oySZLqFplZdgxDIiJ+AzxRdhzSMDsY+G3ZQUjD7HWZ\n+eqygyhDRPw5cEpm/lWx/kHgzZl54S77zQJmFatvAB5twOn9e1Mf26k+tlN9bKf62E4vqes3ctQO\nH23V/yCotUXE/Zk5tew4JA2b9cDhNeuHFWU7ycyFwMJGnti/N/WxnepjO9XHdqqP7TRwDh+VJKl5\n3QdMiogjImIf4GzglpJjkiQ1mVHbUyhJ0miXmd0RcSHwXWAMsDgzHyk5LElSkzEplEaXhg4PkzTy\nZeZtwG0lnNq/N/WxnepjO9XHdqqP7TRAo3aiGUmSJElS/7ynUJIkSZJamEmhNMQiYntEPBQRP42I\nn0TECQ2oc0pEnFaz/umI+B+77LM2Ig7up543FrE9GBF/EBHzIuKRiPhZUf7mYr9KRDxalD1UTIMv\nqQVFxCnF34M1ETG37HjKVvytXVX8bby/KDsoIu6KiF8U7+Nr9r+kaLtHI+Lk8iIfWhGxOCKejoiH\na8oG3C4RcVzRvmsi4osREcP9WYZSH+306YhYX/ObW/t736rtdHhErIiI1cX/Uy4uyv1ONYhJoTT0\nfpeZUzLzGOAS4IoG1DkFOK3fvfp3FvD1zHwT8PvAu4BjM/O/AO9g54din1N8jimZ+fUGnFtSk4mI\nMcCXgVOBycCMiJhcblQjwrTib2PPFPhzgbszcxJwd7FO0VZnA0cCpwDXFG06Gl1H9TPW2pN2+Qpw\nPjCpeO1aZ7O7jt4/0xdqfnNvg5Zvp25gdmZOBt4CXFC0h9+pBjEplIbXgcC/AUTEIRFxT3EV8OGI\n+OOifHNEfL64Eva9iDi+6Kl7LCLOKKad/wzwvuLY9+3uhBExMSK6IuLaos47I2JcceXx48BfR8QK\n4BDgt5m5FSAzf5uZvx7CtpDUfI4H1mTmY5n5AnAjcGbJMY1EZwJLiuUlVC/A9ZTfmJlbM/NxYA3V\nNh11MvMe4NldigfULhFxCHBgZt6b1Ukwrq85ZlToo5360srttCEzf1IsbwK6gAn4nWoYk0Jp6I0r\nkrefA/8IfLYofz/w3cycAhwDPFSU7w98PzOPBDYBlwPvBN4NfKb4j9gngZuKK4g31RHDJODLRZ3/\nDvxZceXxq1SvRk4D7gQOj4h/jYhrIuJtu9RxQ81Qlt/bs6aQ1OQmsPMIgnVFWStL4HsR8UBEzCrK\n2jJzQ7H8JNBWLLd6+w20XSYUy7uWt4KLonorx+KaIZG2E9WL3cCbgB/hd6phTAqlodczfPSNVIco\nXF+MX78P+MuI+DRwdHHlC+AF4I5ieRXwg8zcVixP7OMcfU0j3FP+eGb2JJ0P9FZPZm4GjgNmAb8B\nboqID9fsUjt89JndfF5JaiUnFRf3TqU6pO2ttRuL3ginet+F7bJbXwFeT/VWkQ3AgnLDGTki4hXA\nN4CPZ+bG2m1+pwbHpFAaRpn5L8DBwKuLISNvBdYD10XEh4rdtuVLz4p5EegZzvkifT9b9Blg/C5l\nB1DtFaSnjsL2vurJzO2ZWcnMTwEXAn9W72eT1BLWA4fXrB9WlLWszFxfvD8NfIvqcNCnimFqFO9P\nF7u3evsNtF3WF8u7lo9qmflU8Xv8InAtLw0xbul2ioi9qSaEN2TmN4tiv1MNYlIoDaOIeCMwBngm\nIl4HPJWZ11IdVnrsAKraRDXp63EPcEZEHFCc5z3ATzNz+wBie0NETKopmgI8MYCYJI1+9wGTIuKI\n4v7ms4FbSo6pNBGxf83f3f2BPwEeptom5xa7nQssL5ZvAc6OiH0j4giqQ/t/PLxRl2pA7VIMC9wY\nEW8pRth8qOaYUasnySm8m+p3Clq4nYrPtQjoysyrajb5nWqQvnodJDXOuIjoGboZwLmZuT0iOoD/\nGRHbgM1U/zDVawUwt6j3isy8KSK+BKyMiKR6peyvBhjnK4CrI+JVVGf5WkN1KKkkAZCZ3RFxIfBd\nqhe4FmfmIyWHVaY24FvFjPZjga9l5h0RcR9wc0TMpHpx7b0AmflIRNwMrKb6d/aCgVy8ayYRsRTo\nAA6OiHXAp4ArGXi7fIzqDJ3jgNuL16jRRzt1RMQUqkMh1wIfgdZuJ+BE4IPAqpr/U12K36mGiZdG\nqUmSJEmSWo3DRyVJkiSphZkUSpIkSVILMymUJEmSpBZmUihJkiRJLcykUJIkSZJamEmhJEmSShER\n2yPioYj4aUT8JCJOaECdUyLitJr1T0fE/9hln7URcXA/9byxiO3BiPiDiJgXEY9ExM+K8jcX+1Ui\n4tGi7KGI+PPBfgZpuPmcQkmSJJXld5k5BSAiTgauAN42yDqnAFOB2wZZz1nA1zPz8oj4r8C7gGMz\nc2uRUO5Ts+85mXn/IM8nlcaeQkmSJI0EBwL/BhARh0TEPUXP28MR8cdF+eaI+HzRY/e9iDi+6Kl7\nLCLOiIh9gM8A7yuOfd/uThgREyOiKyKuLeq8MyLGFT2NHwf+OiJWAIcAv83MrQCZ+dvM/PUQtoU0\nrEwKJUmSVJZxRfL2c+Afgc8W5e8Hvlv0Ih4DPFSU7w98PzOPBDYBlwPvBN4NfCYzXwA+CdyUmVMy\n86Y6YpgEfLmo89+BP8vM24CvAl/IzGnAncDhEfGvEXFNROzam3lDzfDR39uzppDK4/BRSZIklaV2\n+Oh/Ba6PiKOA+4DFEbE3sCwze5LCF4A7iuVVwNbM3BYRq4CJfZwj+yl/vKb+B3qrJzM3R8RxwB8D\n04CbImJuZl5X7OLwUTU1ewolSZJUusz8F+Bg4NWZeQ/wVmA9cF1EfKjYbVtm9iRzLwI9wzlfpO/O\njmeA8buUHUC1V5CeOgrb+6onM7dnZiUzPwVcCPxZvZ9NGulMCiVJklS6iHgjMAZ4JiJeBzyVmddS\nHVZ67ACq2kQ16etxD3BGRBxQnOc9wE8zc/sAYntDREyqKZoCPDGAmKQRzeGjkiRJKsu4iOgZuhnA\nuZm5PSI6gP8ZEduAzcCH+qqgFyuAuUW9V2TmTRHxJWBlRCTwNPBXA4zzFcDVEfEqoBtYA8waYB3S\niBUv9cBLkiRJklqNw0clSZIkqYWZFEqSJElSCzMplCRJkqQWZlIoSZIkSS3MpFCSJEmSWphJoSRJ\nkiS1MJNCSZIkSWphJoWSJEmS1ML+f/G2PLmSumz2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461df1c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMUOhJEmSJI2YoVCSJEmSRuwXsMGCisISFBIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461d981c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYlSNMk/ID4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461dc10080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCAgOv2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461de4feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAF3CAYAAAD914WlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERER0aX+A0ky2M3LWWp2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461de40ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+cXWV96PvPl0mAEOXXUefGBIna2E4SriCRcjW3nQFR\nLAq2qM34C8oIWjHSc7jVQFqx9UxL9WBbqWiD4RDukeGAiCDhhxAyenPkp0ghMFKiBCEnGFQEEmtI\nJt/7x14Dm8kk2ZPMzJo96/N+vfZrr/WsX9/9ZDJ7vut51vNEZiJJkiRJqqa9yg5AkiRJklQek0JJ\nkiRJqjCTQkmSJEmqMJNCSZIkSaowk0JJkiRJqjCTQkmSJEmqMJNCSZIkSaowk0JJkiRJqjCTQkmS\nJEmqMJNCSZIkSaqwSWUHMFpe8YpX5MyZM8sOQxpTmzZtYurUqWWHIY2pH/7wh7/IzFeWHUezqOL3\no78brQOwDsA6GFClemj0O3LCJoUzZ87knnvuKTsMaUz19vbS3t5edhjSmIqIx8qOoZlU8fvR343W\nAVgHYB0MqFI9NPodafdRSZIkSaowk0JJkiRJqjCTQkmSJEmqMJNCSZIkSaowk0JJkiRJqjCTQkmS\nJEmqMJNCSZIkSaowk0JJkiRJqjCTQkmSJEmqMJNCaQLo6elh7ty5HHvsscydO5eenp6yQ5IkSVKT\nmFR2AJL2TE9PD4sXL2bp0qX09/fT0tJCV1cXAJ2dnSVHJ0mSpPHOlkKpyXV3d7N06VI6OjqYNGkS\nHR0dLF26lO7u7rJDkyRJUhMwKZSaXF9fH/Pnz39J2fz58+nr6yspIkmSJDUTu49KTa6trY1Vq1bR\n0dHxQtmqVatoa2srMSpJuyMi9gW+D+xD7Tv6m5l5XkQcDPxPYCawFnh/Zj5dHHMO0AX0A5/KzJtL\nCF3AzEXL9/gca88/YQQikaThsaVQanKLFy+mq6uLlStXsnXrVlauXElXVxeLFy8uOzRJw7cZOCYz\n3wgcDhwfEUcDi4AVmTkLWFGsExGzgQXAHOB44KKIaCklcklS07KlUGpyA4PJLFy4kL6+Ptra2uju\n7naQGakJZWYCG4vVycUrgZOA9qJ8GdALfKYovyIzNwOPRsQa4Cjg9rGLWpLU7GwplCaAzs5OVq9e\nzYoVK1i9erUJodTEIqIlIu4DNgC3ZOadQGtmri92eRJoLZanA4/XHf5EUSZJUsNsKZQkaRzJzH7g\n8Ig4ELgmIuYO2p4RkcM5Z0ScAZwB0NraSm9v70iF2xQ2btw4Jp/57MO27vE5RivOsaqD8cw6sA4G\nWA/bMymUJGkcysxfR8RKas8K/jwipmXm+oiYRq0VEWAdcEjdYTOKssHnWgIsAZg3b162t7ePauzj\nTW9vL2PxmU8diYFmPti+54EMYazqYDyzDqyDAdbD9uw+KknSOBERryxaCImIKcBxwI+B64BTit1O\nAa4tlq8DFkTEPhHxWmAWcNfYRi1Jana2FEqSNH5MA5YVI4juBVyZmddHxO3AlRHRBTwGvB8gMx+M\niCuBh4CtwJlF91NJkhpmUihJ0jiRmfcDRwxR/kvg2B0c0w10j3JokqQJzKRQkiRV2khMOi9Jzcxn\nCiVJkiSpwkwKJUmSJKnCTAolSZIkqcJMCiVJkiSpwkwKJUmSJKnCTAolSZIkqcJMCiVJkiSpwkwK\nJUmSJKnCTAolSZIkqcJMCiVJkiSpwkwKJUmSJKnCTAolSZIkqcJMCiVJkiSpwkwKJUmSJKnCTAol\nSZIkqcJMCiVJkiSpwkwKJUmSJKnCTAolSZIkqcJMCiVJkiSpwkwKJUmSJKnCTAolSZIkqcJGNSmM\niLUR8UBE3BcR9xRlB0fELRHxSPF+UN3+50TEmoh4OCLeUVd+ZHGeNRHx5YiI0YxbkiRJkqpiLFoK\nOzLz8MycV6wvAlZk5ixgRbFORMwGFgBzgOOBiyKipTjmq8DpwKzidfwYxC1JkiRJE14Z3UdPApYV\ny8uA99SVX5GZmzPzUWANcFRETAP2z8w7MjOBy+qOkSRJkiTtgdFOChO4NSJ+GBFnFGWtmbm+WH4S\naC2WpwOP1x37RFE2vVgeXC5JkiRJ2kOTRvn88zNzXUS8CrglIn5cvzEzMyJypC5WJJ5nALS2ttLb\n2ztSp5aawsaNG/25lyRJ0rCMalKYmeuK9w0RcQ1wFPDziJiWmeuLrqEbit3XAYfUHT6jKFtXLA8u\nH+p6S4AlAPPmzcv29vYR/DTS+Nfb24s/95IkSRqOUes+GhFTI+LlA8vA24HVwHXAKcVupwDXFsvX\nAQsiYp+IeC21AWXuKrqaPhsRRxejjn6k7hhJkiRJ0h4YzZbCVuCaYvaIScDlmXlTRNwNXBkRXcBj\nwPsBMvPBiLgSeAjYCpyZmf3FuT4BXApMAW4sXpIkSZKkPTRqSWFm/hR44xDlvwSO3cEx3UD3EOX3\nAHNHOkZJkiRJqroypqSQJEmSJI0TJoWSJEmSVGEmhZIkSZJUYSaFkiRJklRhJoWSJEmSVGEmhZIk\nSZJUYaM5T6EkSdKomrlo+S73OfuwrZzawH6SVFW2FEqSJElShZkUSpIkSVKFmRRKkiRJUoWZFEoT\nQE9PD3PnzuXYY49l7ty59PT0lB2SJEmSmoQDzUhNrqenh8WLF7N06VL6+/tpaWmhq6sLgM7OzpKj\nkzQcEXEIcBnQCiSwJDP/OSI+B5wOPFXsem5m3lAccw7QBfQDn8rMm8c8cElSU7OlUGpy3d3dLF26\nlI6ODiZNmkRHRwdLly6lu7u77NAkDd9W4OzMnA0cDZwZEbOLbf+YmYcXr4GEcDawAJgDHA9cFBEt\nZQQuSWpeJoVSk+vr62P+/PkvKZs/fz59fX0lRSRpd2Xm+sy8t1h+DugDpu/kkJOAKzJzc2Y+CqwB\njhr9SCVJE4lJodTk2traWLVq1UvKVq1aRVtbW0kRSRoJETETOAK4syhaGBH3R8QlEXFQUTYdeLzu\nsCfYeRIpSdJ2fKZQanKLFy+mq6vrhWcKV65cSVdXl91HpSYWES8Drgb+IjOfjYivAp+n9pzh54EL\ngNOGcb4zgDMAWltb6e3tHfGYy3L2YVt3uU/rlMb2Gw9G699m48aNE+rffXdYB9bBAOtheyaFUpMb\nGExm4cKF9PX10dbWRnd3t4PMSE0qIiZTSwi/kZnfAsjMn9dtvxi4vlhdBxxSd/iMouwlMnMJsARg\n3rx52d7ePiqxl+HURct3uc/Zh23lggea40+etR9sH5Xz9vb2MpH+3XeHdWAdDLAetmf3UWkC6Ozs\nZPXq1axYsYLVq1ebEEpNKiICWAr0ZeaX6sqn1e32x8DqYvk6YEFE7BMRrwVmAXeNVbySpImhOW6b\nSZJUDW8FPgw8EBH3FWXnAp0RcTi17qNrgY8BZOaDEXEl8BC1kUvPzMz+MY9aktTUTAolSRonMnMV\nEENsumEnx3QDPkQsSdptdh+VJEmSpAozKZQkSZKkCjMplCaAnp4e5s6dy7HHHsvcuXPp6ekpOyRJ\nkiQ1CZ8plJpcT08PixcvfmGewpaWFrq6ugAchVSSJEm7ZEuh1OS6u7tZunQpHR0dTJo0iY6ODpYu\nXerk9ZIkSWqISaHU5Pr6+pg/f/5LyubPn09fX19JEUmSJKmZmBRKTa6trY1Vq1a9pGzVqlW0tbWV\nFJEkSZKaiUmh1OQWL15MV1cXK1euZOvWraxcuZKuri4WL15cdmiSJElqAg40IzW5gcFkFi5cSF9f\nH21tbXR3dzvIjCRJkhpiUihNAJ2dnXR2dtLb20t7e3vZ4UiSJKmJ2H1UkiRJkirMpFCaAJy8XpIk\nSbvL7qNSk3PyekmSJO0JWwqlJufk9ZIkSdoTJoVSk3PyekmSJO0Ju49KTW5g8vqOjo4Xypy8XpKa\n08xFy/f4HGvPP2EEIpFUJbYUSk3OyeslSZK0J2wplJqck9dLkiRpT5gUShOAk9dLkiRpd9l9VJIk\nSZIqzKRQkiRJkirMpFCSJEmSKsykUJIkSZIqzKRQkiRJkirMpFCSJEmSKsykUJIkSZIqzKRQkiRJ\nkips1JPCiGiJiB9FxPXF+sERcUtEPFK8H1S37zkRsSYiHo6Id9SVHxkRDxTbvhwRMdpxS5IkSVIV\njEVL4VlAX936ImBFZs4CVhTrRMRsYAEwBzgeuCgiWopjvgqcDswqXsePQdySJEmSNOGNalIYETOA\nE4Cv1xWfBCwrlpcB76krvyIzN2fmo8Aa4KiImAbsn5l3ZGYCl9UdI0mSJEnaA6PdUvhPwKeBbXVl\nrZm5vlh+EmgtlqcDj9ft90RRNr1YHlwuSZIkSdpDk0brxBHxLmBDZv4wItqH2iczMyJyBK95BnAG\nQGtrK729vSN1aqkpbNy40Z97SZIkDcuoJYXAW4ETI+KPgH2B/SPifwA/j4hpmbm+6Bq6odh/HXBI\n3fEzirJ1xfLg8u1k5hJgCcC8efOyvb19BD+ONP719vbiz71Uvoh4K3BfZm6KiA8BbwL+OTMfKzk0\nSZK2M2rdRzPznMyckZkzqQ0gc1tmfgi4Djil2O0U4Npi+TpgQUTsExGvpTagzF1FV9NnI+LoYtTR\nj9QdI0nSePRV4DcR8UbgbOAn1J6JlyRp3CljnsLzgeMi4hHgbcU6mfkgcCXwEHATcGZm9hfHfILa\nYDVrqH2x3jjWQUuSNAxbi8HRTgL+JTO/Ary85JgkSRrSaHYffUFm9gK9xfIvgWN3sF830D1E+T3A\n3NGLUJKkEfVcRJwDfAj4g4jYC5hcckySJA2pjJZCSZImuj8FNgNdmfkktefhv1huSJIkDW1MWgol\nSaqKiGgBejKzY6AsM3+GzxRKksYpWwolSRpBxfPw2yLigLJjkSSpEbYUSpI08jYCD0TELcCmgcLM\n/FR5IUmSNDSTQkmSRt63ipckSeOeSaEkSSMsM5dFxBTgNZn5cNnxSJK0Mz5TKEnSCIuIdwP3UZt3\nl4g4PCKua+C4QyJiZUQ8FBEPRsRZRfnBEXFLRDxSvB9Ud8w5EbEmIh6OiHeM1meSJE1cJoWSJI28\nzwFHAb8GyMz7gNc1cNxW4OzMnA0cDZwZEbOBRcCKzJwFrCjWKbYtAOYAxwMXFaOfSpLUMJNCSZJG\n3pbMfGZQ2bZdHZSZ6zPz3mL5OaAPmA6cBCwrdlsGvKdYPgm4IjM3Z+ajwBpqyagkSQ0zKZQkaeQ9\nGBEfAFoiYlZEXAj8YDgniIiZwBHAnUBrZq4vNj0JtBbL04HH6w57oiiTJKlhDjQjSdLIWwgsBjYD\nPcDNwOcbPTgiXgZcDfxFZj4bES9sy8yMiBxOMBFxBnAGQGtrK729vcM5fFw7+7Ctu9yndUpj+00U\nQ/37bty4cUL9u+8O68A6GGA9bM+kUJKkEZaZv6GWFC4e7rERMZlaQviNzByY1uLnETEtM9dHxDRg\nQ1G+Djik7vAZRdngeJYASwDmzZuX7e3tww1r3Dp10fJd7nP2YVu54IHq/Mmz9oPt25X19vYykf7d\nd4d1YB0MsB62V53fkJIkjZGI+A4wuDXvGeAe4F8z87c7OC6ApUBfZn6pbtN1wCnA+cX7tXXll0fE\nl4BXA7OAu0bqc0iSqsFnCqUJoKenh7lz53Lssccyd+5cenp6yg5JqrqfAhuBi4vXs8BzwBuK9R15\nK/Bh4JiIuK94/RG1ZPC4iHgEeFuxTmY+CFwJPERt+oszM7N/dD6SJGmisqVQanI9PT2cddZZTJ06\nlcxk06ZNnHXWWQB0dnaWHJ1UWW/JzDfXrX8nIu7OzDdHxIM7OigzVwGxg83H7uCYbqB790OVJFWd\nLYVSk/v0pz9NS0sLl1xyCd/97ne55JJLaGlp4dOf/nTZoUlV9rKIeM3ASrH8smL1+XJCkiRpaCaF\nUpN74oknuOyyy+jo6GDSpEl0dHRw2WWX8cQTT5QdmlRlZwOrImJlRPQC/x/w/0TEVF6cb1CSpHHB\n7qOSJI2wzLwhImYBv1cUPVw3uMw/lRSWJElDMimUmtyMGTM45ZRT+MY3vkF/fz8rV67klFNOYcaM\nGWWHJlXdkcBMat+1b4wIMvOyckOSJGl7JoVSk/vCF77AWWedxWmnncbPfvYzXvOa17B161YuuOCC\nskOTKisi/l/g9cB9wMBooAmYFEqSxh2TQqnJDYww2t1dG3xw6tSp/N3f/Z0jj0rlmgfMzszBcxVK\nkjTuONCMJEkjbzXwf5QdhCRJjbClUGpyPT09LF68mKVLl9Lf309LSwtdXV2A8xRKJXoF8FBE3AVs\nHijMzBPLC0mSpKGZFEpNrru7mw984AMsXLiQvr4+2tra+MAHPkB3d7dJoVSez5UdgCRJjTIplJrc\nQw89xG9+85vtWgrXrl1bdmhSZWXm9yLiUGBWZt4aEfsBLWXHJUnSUHymUGpye++9N5/85CdfMnn9\nJz/5Sfbee++yQ5MqKyJOB74J/GtRNB34dnkRSZK0Y7YUSk3u+eef5+///u+58MILX5iSYuPGjTz/\n/PNlhyZV2ZnAUcCdAJn5SES8qtyQJEkami2FUpObPn06W7ZsAWBg9PstW7Ywffr0MsOSqm5zZr5w\nZyYiJlGbp1CSpHHHlkJpAthvv/245JJLXnim8IMf/GDZIUlV972IOBeYEhHHAZ8AvlNyTJIkDanh\npDAiTgDmAPsOlGXm345GUJIa97//9//m0ksvfcnoo//wD//AqaeeWnZoUpUtArqAB4CPATcAXy81\nIkmSdqCh7qMR8TXgT4GFQADvAw4dxbgkNaitrY0ZM2awevVqVqxYwerVq5kxYwZtbW1lhyZVVmZu\ny8yLM/N9wBnAnTnQv1uSpHGm0WcK35KZHwGezsy/Af4v4A2jF5akRi1evJiuri5WrlzJ1q1bWbly\nJV1dXSxevLjs0KTKiojeiNg/Ig4GfghcHBH/WHZckiQNpdHuo/9RvP8mIl4N/BKYNjohSRqOzs5O\nfvCDH/DOd76TzZs3s88++3D66ac7cb1UrgMy89mI+ChwWWaeFxH3lx2UJElDaTQpvD4iDgS+CNxL\nbQQ1n42QxoGenh6WL1/OjTfe+JLJ69/ylreYGErlmRQR04D3AzbbS5LGtYa6j2bm5zPz15l5NbVn\nCX8vM/96dEOT1Iju7m6WLl36ksnrly5dSnd3d9mhSVX2t8DNwJrMvDsiXgc8UnJMkiQNqdGBZvaL\niL+OiIszczPwqoh41yjHJqkBfX19XHXVVey77750dHSw7777ctVVV9HX11d2aFJlZeZVmfl/ZuYn\nivWfZubJZcclSdJQGh1o5r8Dm6kNMAOwDvivoxKRpGE58MAD+drXvsaBBx445LqksRcRXygGmpkc\nESsi4qmI+FDZcUmSNJRGk8LXZ+YXgC0AmfkbalNTSCrZM888w8BI9xG1/5aZyTPPPFNmWFLVvT0z\nnwXeBawFfgf4y1IjkiRpBxpNCp+PiCnUBpghIl5PreVQUsn6+/vZf//9mTJlChHBlClT2H///env\n7y87NKnKBgZyOwG4KjO9SyNJGrcaTQrPA24CDomIbwArgE+PWlSShmXBggU8+uijrFixgkcffZQF\nCxaUHZJUdddHxI+BI4EVEfFK4LclxyRJ0pB2OSVF1Pqj/Rj4E+Boat1Gz8rMX4xybJIadPHFF3Pt\ntdeyYcMGXvWqV7Fhw4ayQ5IqLTMXRcQXgGcysz8iNgEnlR2XJElD2WVSmJkZETdk5mHA8jGISdIw\nHHzwwfzqV7/iF7/4BZn5wvvBBx9cdmhS1b0aeFtE7FtXdllZwUiStCONTl5/b0S8OTPvHtVoJA3b\nfvvtx29/+1u2bNlCf38/e+21F/vssw/77bdf2aFJlRUR5wHtwGzgBuCdwCpMCiVJ41CjzxT+PnB7\nRPwkIu6PiAci4v7RDExSY9atW8e2bdvYsmULAFu2bGHbtm2sW7eu5MikSnsvcCzwZGb+GfBG4IBy\nQ5IkaWiNthS+Y1SjkLTbIoLnn3+eCy64gNmzZ/PQQw/xl3/5ly9MTyGpFP+RmdsiYmtE7A9sAA4p\nOyhJkobSUEthZj428AJ+AfzfwEWjGpmkhmzbto0DDjiAI444gkmTJnHEEUdwwAEHsG3btrJDk6rs\nnog4ELgY+CFwL3B7uSFJkjS0hloKI2JvanMtfYBaq+HVwNdGMS5Jw/DRj36UhQsX0tfXR1tbGx/9\n6Ef54he/WHZYUmVl5ieKxa9FxE3A/pnpYxeSpHFpp0lhRLwd6ATeDqyk9oD8m4vnI3aqGG3t+8A+\nxXW+mZnnRcTBwP8EZgJrgfdn5tPFMecAXUA/8KnMvLkoPxK4FJhC7YH9szIzh/lZpQlp0qRJfP3r\nX+fqq6+mv7+flpYWTj75ZCZNarR3uKTREBF/AswHktogMyaFkqRxaVfdR28CXgfMz8wPZeZ3gEb7\npG0GjsnMNwKHA8dHxNHAImBFZs4CVhTrRMRsYAEwBzgeuCgiWopzfRU4HZhVvI5vMAZpwvv4xz/O\nM888Q2dnJ8cddxydnZ0888wzfPzjHy87NKmyIuIi4OPAA8Bq4GMR8ZVyo5IkaWi7akp4E7VE7daI\n+ClwBdCy80Nqipa8jcXq5OKV1CbvbS/KlwG9wGeK8isyczPwaESsAY6KiLXUut3cARARlwHvAW5s\nJA5porvwwgv593//d2655RYANmzYwHHHHceFF15YcmRSpR0DtA30aomIZcCD5YYkSdLQdtpSmJn3\nZeaizHw9cB61Fr/JEXFjRJyxq5NHREtE3Edt1LVbMvNOoDUz1xe7PAm0FsvTgcfrDn+iKJteLA8u\nlwT09PTwyCOPsGLFCm655RZWrFjBI488Qk9PT9mhSVW2BnhN3fohRZkkSeNOww8dZeYPgB9ExFnA\n26i1IC7ZxTH9wOHFCGzXRMTcQdszIkbs2cAiUT0DoLW1ld7e3pE6tTRunXvuucyfP5/TTjuNn/3s\nZ7zmNa9h/vz5nHvuuUybNq3s8KSqejnQFxF3UeslcxS1EUmvA8jME8sMTpKkeo2OPvodoAe4NjM3\nAd8tXg3JzF9HxEpqzwL+PCKmZeb6iJhGrRURYB0vncNpRlG2rlgeXD7UdZZQJKrz5s3L9vb2RkOU\nmtZjjz0GwCWXXPLCQDOnnXYajz32GP4fkErz2bIDkCSpUQ3NUwj8N2ojqD0UEd+MiPcWo4vuUES8\nsmghJCKmAMcBPwauA04pdjsFuLZYvg5YEBH7RMRrqQ0oc1fR1fTZiDg6arNxf6TuGKny9t57bxYu\nXEhHRweTJk2io6ODhQsXsvfee5cdmlRZmfm9nb3Kjk+SpHoNtRQWX2DfK0YDPYbaSKCXAPvv5LBp\nwLLimL2AKzPz+oi4HbgyIrqAx4D3F9d4MCKuBB4CtgJnFt1PAT7Bi1NS3IiDzEgveP755zn//PO5\n8MILX+g+umnTJp5//vmyQ5MkSVITaPiZwqK1793An1IblXTZzvYvJuk9YojyXwLH7uCYbqB7iPJ7\ngLnbHyFp+vTprF+/nqeeegqAtWvX0tLSwvTpjsckSZKkXWuo+2jRgtdHrZXwX4DXZ+bC0QxMUmOe\neuop+vv7OfHEE7nmmms48cQT6e/vfyFJlDR2ImJF8f4PZcciSVKjGm0pXAp01nXnlDRObN68mWOO\nOYaf/OQnnHzyybS1tXHMMcdw2223lR2aVEXTIuItwIkRcQUQ9Rsz895ywpIkacd2mhRGxDGZeRsw\nFTipNs7LizLzW6MYm6QG7bvvvqxZs4Zt27axZs0aDj300LJDkqrqs8BfUxsp+0uDtiW1Hjc7FBGX\nAO8CNmTm3KLsc9Se5R9o/j83M28otp0DdAH9wKcy8+aR+RiSpCrZVUvhHwK3UXuWcLAETAqlceCG\nG27goIMOYsuWLey3337ccMMNZYckVVJmfhP4ZkT8dWZ+fjdOcSm1xzQuG1T+j5n53+oLImI2tTmD\n5wCvBm6NiDfYq0eSNFw7TQoz87zi/c8Gb4uIk0crKEnD9/TTT7/kXVJ5MvPzEXEi8AdFUW9mXt/A\ncd+PiJkNXuYk4IrM3Aw8GhFrgKOA23cjZElShTU6T+FQ/nHEopAkaQKJiL8HzqI2zdJDwFkR8Xd7\ncMqFEXF/RFwSEQcVZdOBx+v2eaIokyRpWBqekmIIsetdJI2FyZMnc/PNN9Pf309LSwvveMc72LJl\nS9lhSVV2AnB4Zm4DiIhlwI+Ac3fjXF8FPk/tsY3PAxcApw3nBBFxBnAGQGtrK729vbsRxvh09mFb\nd7lP65TG9psohvr33bhx44T6d98d1oF1MMB62N6eJIU5YlFI2iNbtmzhRz/6EbNnz+b+++83IZTG\nhwOBXxXLB+zuSTLz5wPLEXExMNANdR1wSN2uM4qyoc6xBFgCMG/evGxvb9/dcMadUxct3+U+Zx+2\nlQse2JM/eZrL2g+2b1fW29vLRPp33x3WgXUwwHrY3q5GH32AoZO/AFpHJSJJwzZ58mQWLVrEli1b\nmDx5MpMnTzYxlMr198CPImIlte/MPwAW7c6JImJaZq4vVv8YWF0sXwdcHhFfojbQzCzgrj2KWpJU\nSbu6bfauMYlC0m6bOnUqmzZt4qCDDuLpp5/mZS97GU8//TRTp04tOzSpsjKzJyJ6gTcXRZ/JzCd3\ndVxE9ADtwCsi4gngPKA9Ig6ndpN2LfCx4hoPRsSV1J5Z3Aqc6cijkqTdsavRRx+LiBbg1szsGKOY\npMobPCdoIwaPPrpp06ZhnSfTHuHSSCpa964b5jGdQxQv3cn+3UD3MEOTJOkldjn6aHHXcVtE7Pbz\nEJKGJzOH9br88suZM2cOxF7MmTOHyy+/fNjnkCRJUjU1+tT1RuCBiLgF2DRQmJmfGpWoJA1LZ2cn\nnZ2dzFy0nNXnn1B2OJIkSWoijSaF3ype8OLAM05JIUnSIMVjFw9m5u+VHYskSY3Y1eijJwEzMvMr\nxfpdwCupJYafGf3wJElqLpnZHxEPR8RrMvNnZccjSdKu7Kql8NPAgrr1vYEjgZcB/x24apTikiSp\nmR0EPFjcTK1/7OLE8kKSJGlou0oK987Mx+vWV2Xmr4BfRYTj3UuSNLS/LjsASZIatauk8KD6lcz8\nZN3qK0fDMDREAAAVpUlEQVQ+HEmSml9mfi8iDgVmZeatEbEf0FJ2XJIkDWVXU1LcGRGnDy6MiI8B\nd41OSJIkNbfiu/ObwL8WRdOBb5cXkSRJO7arlsL/DHw7Ij4A3FuUHQnsA7xnNAOTJKmJnQkcBdwJ\nkJmPRMSryg1JkqSh7TQpzMwNwFsi4hhgTlG8PDNvG/XIJElqXpsz8/mI2uxNETGJF6d0kiRpXGlo\nnsIiCTQRlCSpMd+LiHOBKRFxHPAJ4DslxyRJ0pB29UyhJEkavkXAU8ADwMeAG4C/KjUiSZJ2oKGW\nQkmS1LjM3BYRy6g9U5jAw5lp91FJ0rhkUihJ0giLiBOArwE/AQJ4bUR8LDNvLDcySZK2Z1IoSdLI\nuwDoyMw1ABHxemA5YFIoSRp3fKZQkqSR99xAQlj4KfBcWcFIkrQzthRKkjRCIuJPisV7IuIG4Epq\nzxS+D7i7tMAkSdoJk0JJkkbOu+uWfw78YbH8FDBl7MORJGnXTAolSRohmflnZccgSdJwmRRKkjTC\nIuK1wEJgJnXftZl5YlkxSZK0IyaFkiSNvG8DS4HvANtKjkWSpJ0yKZQkaeT9NjO/XHYQqqaZi5Zv\nV3b2YVs5dYjynVl7/gkjFZKkcc6kUJKkkffPEXEe8F1g80BhZt5bXkiSJA3NpFCSpJF3GPBh4Bhe\n7D6axbokSeOKSaEkSSPvfcDrMvP5sgORJGlX9io7AEmSJqDVwIFlByFJUiNsKZQkaeQdCPw4Iu7m\npc8UOiWFJGncMSmUJGnknVd2AJIkNcqkUJKkEZaZ3ys7BkmSGmVSKEnSCIuI56iNNgqwNzAZ2JSZ\n+5cXlSRJQzMplCRphGXmyweWIyKAk4Cjy4tIkqQdc/RRSZJGUdZ8G3hH2bFIkjQUWwolSRphEfEn\ndat7AfOA35YUjiRJO2VSKEnSyHt33fJWYC21LqSSJI07JoWSJI2wzPyzsmOQJKlRJoWSJI2QiPjs\nTjZnZn5+zIKRJKlBozbQTEQcEhErI+KhiHgwIs4qyg+OiFsi4pHi/aC6Y86JiDUR8XBEvKOu/MiI\neKDY9uViJDdJksabTUO8ALqAz5QVlCRJOzOao49uBc7OzNnUhuE+MyJmA4uAFZk5C1hRrFNsWwDM\nAY4HLoqIluJcXwVOB2YVr+NHMW5JknZLZl4w8AKWAFOAPwOuAF5XanCSJO3AqCWFmbk+M+8tlp8D\n+oDp1B60X1bstgx4T7F8EnBFZm7OzEeBNcBRETEN2D8z78jMBC6rO0aSpHGl6BHzX4H7qT2m8abM\n/Exmbig5NEmShjQmzxRGxEzgCOBOoDUz1xebngRai+XpwB11hz1RlG0plgeXS5I0rkTEF4E/odZK\neFhmbiw5JEmSdmnUk8KIeBlwNfAXmfls/eOAmZkRkSN4rTOAMwBaW1vp7e0dqVNLTcOfe6lUZwOb\ngb8CFtd95wW1r739ywpMkqQdGdWkMCImU0sIv5GZ3yqKfx4R0zJzfdE1dKA7zTrgkLrDZxRl64rl\nweXbycwl1O7OMm/evGxvbx+pjyI1h5uW48+9VJ7MHM1n9SVJGhWjOfpoAEuBvsz8Ut2m64BTiuVT\ngGvryhdExD4R8VpqA8rcVXQ1fTYiji7O+ZG6YyRJmjAi4pKI2BARq+vKhj1qtyRJwzGaLYVvBT4M\nPBAR9xVl5wLnA1dGRBfwGPB+gMx8MCKuBB6iNnLpmZnZXxz3CeBSaqO43Vi8JEmaaC4F/oXaoGoD\nBkbtPj8iFhXrnxk0avergVsj4g11353j3sxFy8sOQZLEKCaFmbmK2jMUQzl2B8d0A91DlN8DzB25\n6CRJGn8y8/vF4Gz1TgLai+VlQC+1OQ9fGLUbeDQi1gBHAbePRaySpInDZx8kSRrfdjZq9+N1+zk6\ntyRpt4zJlBSSJGnP7e6o3eN1dO6zD9s6JtdpnTJ21xqvdqcOxsvPyUjZuHHjhPtMw2Ud1FgP2zMp\nlCRpfBvuqN3bGa+jc586Rs8Unn3YVi54oNp/8uxOHaz9YPvoBFOS3t7eyo/QbR3UWA/bs/uoJEnj\n27BG7S4hPklSk6v2bTNJksaRiOihNqjMKyLiCeA8dm/UbkmSGmZSKEnSOJGZnTvYNKxRuyVJGg67\nj0qSJElShdlSKI2SN/7Nd3nmP7aM+XXHcjLoA6ZM5t/Oe/uYXU+SJEkjz6RQGiXP/McW1p5/wphe\nc6xH0xrLBFSSJEmjw+6jkiRJklRhJoWSJEmSVGEmhZIkSZJUYSaFkiRJklRhJoWSJEmSVGEmhZIk\nSZJUYSaFkiRJklRhJoWSJEmSVGEmhZIkSZJUYSaFkiRJklRhJoWSJEmSVGEmhZIkSZJUYSaFkiRJ\nklRhJoWSJEmSVGEmhZIkSZJUYSaFkiRJklRhk8oOQJqoXt62iMOWLRr7Cy8bu0u9vA3ghLG7oCRJ\nkkacSaE0Sp7rO5+1549twtTb20t7e/uYXW/mouVjdi1JkiSNDruPSpIkSVKFmRRKkiRJUoWZFEqS\nJElShZkUSpIkSVKFmRRKkiRJUoWZFEqSJElShZkUSpIkSVKFmRRKkiRJUoWZFEqSJElShU0qOwBJ\nkiSNPzMXLd/jc6w9/4QRiETSaLOlUJIkSZIqzKRQkiRJkirMpFCSJEmSKsykUJIkSZIqzKRQkiRJ\nkirMpFCSJEmSKsykUJIkSZIqzHkKpVE0EnM8DdtNY3fNA6ZMHrNrSZIkaXSYFEqjpIwJe2cuWu5E\nwZIkSRoWu49KkiRJUoWNWlIYEZdExIaIWF1XdnBE3BIRjxTvB9VtOyci1kTEwxHxjrryIyPigWLb\nlyMiRitmSZIkSaqa0WwpvBQ4flDZImBFZs4CVhTrRMRsYAEwpzjmoohoKY75KnA6MKt4DT6nJEkT\nXkSsLW6S3hcR9xRlO7zZKklSo0YtKczM7wO/GlR8ErCsWF4GvKeu/IrM3JyZjwJrgKMiYhqwf2be\nkZkJXFZ3jCRJVdORmYdn5rxifcibrZIkDcdYP1PYmpnri+UngdZieTrweN1+TxRl04vlweWSJGnH\nN1slSWpYaaOPZmZGRI7kOSPiDOAMgNbWVnp7e0fy9FJT8OdemrASuDUi+oF/zcwl7PhmqyRJDRvr\npPDnETEtM9cXXUM3FOXrgEPq9ptRlK0rlgeXD6n4glwCMG/evGxvbx/B0KUmcNNy/LmXJqz5mbku\nIl4F3BIRP67fuLObreP1punZh20dk+u0Thm7a41XZdXBePlZA9i4ceO4iqcM1kGN9bC9sU4KrwNO\nAc4v3q+tK788Ir4EvJragDJ3ZWZ/RDwbEUcDdwIfAS4c45glSSpdZq4r3jdExDXAUez4ZuvgY8fl\nTdNTFy0fk+ucfdhWLnig2lMzl1UHaz/YPubX3JHe3t7K3zi1Dmqsh+2N5pQUPcDtwO9GxBMR0UUt\nGTwuIh4B3lask5kPAlcCDwE3AWdmZn9xqk8AX6c2+MxPgBtHK2ZJksajiJgaES8fWAbeDqzmxZut\n8NKbrZIkNWzUbhllZucONh27g/27ge4hyu8B5o5gaJIkNZtW4Jpiqt5JwOWZeVNE3A1cWdx4fQx4\nf4kxSpKaVLX7UkiS1AQy86fAG4co/yU7uNkqSVKjxnpKCkmSJEnSOGJSKEmSJEkVZlIoSZIkSRVm\nUihJkiRJFeZAM5IkSRoVM0dgLsq1558wApFI2hlbCiVJkiSpwkwKJUmSJKnCTAolSZIkqcJMCiVJ\nkiSpwkwKJUmSJKnCTAolSZIkqcJMCiVJkiSpwkwKJUmSJKnCTAolSZIkqcJMCiVJkiSpwkwKJUmS\nJKnCTAolSZIkqcJMCiVJkiSpwkwKJUmSJKnCTAolSZIkqcJMCiVJkiSpwkwKJUmSJKnCTAolSZIk\nqcJMCiVJkiSpwkwKJUmSJKnCTAolSZIkqcJMCiVJkiSpwkwKJUmSJKnCTAolSZIkqcImlR2AJEmS\ntCMzFy3f43OsPf+EEYhEmrhsKZQkSZKkCrOlUBqHImL3j/2H3TsuM3f7mpIkSWpethRK41Bm7tZr\n5cqVu32sJEmSqsmkUJIkSZIqzKRQkiRJkirMpFCSJEmSKsykUJIkSZIqzKRQkiRJkirMKSkkSZI0\noc1ctJyzD9vKqYuW79F51p5/wghFJI0vthRKkiRJUoWZFEqSJElShZkUSpIkSVKF+UyhJElNLCKO\nB/4ZaAG+npnnj8V1Z+7hs1lSMxqJn3ufS9R4ZEuhJElNKiJagK8A7wRmA50RMbvcqCRJzcaWQkmS\nmtdRwJrM/ClARFwBnAQ8VGpUknZoorU2TrTPU1UmhZIkNa/pwON1608Av19SLJK0W0aqO/p4SS6b\nMVGOzBzTC46ViHgKeKzsOKQx9grgF2UHIY2xQzPzlWUHUYaIeC9wfGZ+tFj/MPD7mfnJQfudAZxR\nrP4u8PCYBlo+fzdaB2AdgHUwoEr10NB35IRtKazqHwiqtoi4JzPnlR2HpDGzDjikbn1GUfYSmbkE\nWDJWQY03/m60DsA6AOtggPWwPQeakSSped0NzIqI10bE3sAC4LqSY5IkNZkJ21IoSdJEl5lbI+KT\nwM3UpqS4JDMfLDksSVKTMSmUJpbKdg+TqiozbwBuKDuOcc7fjdYBWAdgHQywHgaZsAPNSJIkSZJ2\nzWcKJUmSJKnCTAqlMRYRrRFxeUT8NCJ+GBG3R8QfD7HfzIhYPUT530bE2xq4zuERkRFx/EjFLknj\nQURcEhEb6n9HRsTBEXFLRDxSvB9Ut+2ciFgTEQ9HxDvqyo+MiAeKbV+OiBjrz7K7IuKQiFgZEQ9F\nxIMRcVZRXpl6iIh9I+KuiPi3og7+piivTB0MiIiWiPhRRFxfrFexDtYW8d8XEfcUZZWrh91lUiiN\noeIXy7eB72fm6zLzSGqjBc4YtN8On/fNzM9m5q0NXK4TWFW8DxlLRPg7QFIzuhQYfMNrEbAiM2cB\nK4p1ImI2td+zc4pjLoqIluKYrwKnA7OKVzPdRNsKnJ2Zs4GjgTOLz1qletgMHJOZbwQOB46PiKOp\nVh0MOAvoq1uvYh0AdGTm4XXTTVS1HobNPwilsXUM8Hxmfm2gIDMfy8wLI+LUiLguIm6j9otrSBFx\naUS8NyKOj4ir6srb6+4QBvA+4FTguIjYtyifWdwRuwxYDRwSEW+PWmvlvRFxVUS8rNj3sxFxd0Ss\njoglVblTJmn8y8zvA78aVHwSsKxYXga8p678iszcnJmPAmuAoyJiGrB/Zt6RtQEWLqs7ZtzLzPWZ\neW+x/By1hGA6FaqHrNlYrE4uXkmF6gAgImYAJwBfryuuVB3shPXQIJNCaWzNAe7dyfY3Ae/NzD9s\n4Fy3Ar8fEVOL9T8FriiW3wI8mpk/AXqpfVkMmAVclJlzgE3AXwFvy8w3AfcA/6XY718y882ZOReY\nAryrgZgkqSytmbm+WH4SaC2WpwOP1+33RFE2vVgeXN50ImImcARwJxWrh6Lb5H3ABuCWzKxcHQD/\nBHwa2FZXVrU6gNoNgVuj9mjOGUVZFetht5gUSiWKiK8Uz0LcXRTdkpmD734PKTO3AjcB7y66m54A\nXFts7uTFBPEKXtqF9LHMvKNYPhqYDfyv4kv1FODQYltHRNwZEQ9Qa+GcM/xPKEljr7jDX4nh1Yve\nHVcDf5GZz9Zvq0I9ZGZ/Zh5O7TGMoyJi7qDtE7oOIuJdwIbM/OGO9pnodVBnfvGz8E5q3an/oH5j\nhephtzhPoTS2HgROHljJzDMj4hXUWuig1nI3HFcAn6TWjeqezHyu6BN/MnBSRCwGAvhPEfHyIa4R\n1BLRlzx3WHQ3vQiYl5mPR8TngH2HGZskjaWfR8S0zFxfdAHbUJSvAw6p229GUbaOlz7PPVDeNCJi\nMrWE8BuZ+a2iuHL1AJCZv46IldSe/6pSHbwVODEi/oja9/T+EfE/qFYdAJCZ64r3DRFxDXAUFayH\n3WVLoTS2bgP2jYg/ryvbbw/O9z1qXU5P58WWwWOB+zPzkMycmZmHUvujYbsRToE7gLdGxO8ARMTU\niHgDLyaAvyjuQr93D2KUpLFwHbXeDhTv19aVL4iIfSLitdS60N9VdCl7NiKOLp6Z/kjdMeNeEfNS\noC8zv1S3qTL1EBGvjIgDi+UpwHHAj6lQHWTmOZk5IzNnUhs45bbM/BAVqgN44e+Xlw8sA2+nNnZC\npephT9hSKI2hzMyIeA/wjxHxaeApai13n6H23N5gvxsR9X3b//Og8/UXg8ucyou/9DqBawad52rg\nz4HvDzr+qYg4FeiJiH2K4r/KzH+PiIup/UJ9ErgbSRonIqIHaAdeUfyOPA84H7gyIrqAx4D3A2Tm\ngxFxJfAQtRE7z8zM/uJUn6A2kukU4Mbi1SzeCnwYeKDo/g9wLtWqh2nAsqKHzF7AlZl5fUTcTnXq\nYEeq9HMAtWcFrynGxJsEXJ6ZNxWP51SpHnZb1LrXSpIkSZKqyO6jkiRJklRhJoWSJEmSVGEmhZIk\nSZJUYSaFkiRJklRhJoWSJEmSVGEmhZIkSSpdRLRGxOUR8dOI+GFE3B4R282xGxEzI2L1EOV/GxFv\na+A6h0dERsTxIxW71OxMCiVJklSqYqLwbwPfz8zXZeaR1CZjnzFovx3OsZ2Zn83MWxu4XCewqngf\nMpaI8G9kVYo/8JIkSSrbMcDzmfm1gYLMfCwzL4yIUyPiuoi4DVixoxNExKUR8d6IOD4irqorb4+I\n64vlAN4HnAocFxH7FuUzI+LhiLgMWA0cEhFvL1or742IqyLiZcW+n42IuyNidUQsKc4pNTWTQkmS\nJJVtDnDvTra/CXhvZv5hA+e6Ffj9iJharP8pcEWx/Bbg0cz8CdALnFB33CzgosycA2wC/gp4W2a+\nCbgH+C/Ffv+SmW/OzLnAFOBdDcQkjWsmhZIkSRpXIuIrEfFvEXF3UXRLZv6qkWMzcytwE/Duorvp\nCcC1xeZOXkwQr+ClXUgfy8w7iuWjgdnA/4qI+4BTgEOLbR0RcWdEPECthXPO8D+hNL7ssF+2JEmS\nNEYeBE4eWMnMMyPiFdRa6KDWcjccVwCfBH4F3JOZz0VES3GNkyJiMRDAf4qIlw9xjaCWiL7kucOi\nu+lFwLzMfDwiPgfsO8zYpHHHlkJJkiSV7TZg34j487qy/fbgfN+j1uX0dF5sGTwWuD8zD8nMmZl5\nKHA1sN0Ip8AdwFsj4ncAImJqRLyBFxPAXxTPGL53D2KUxg2TQkmSJJUqMxN4D/CHEfFoRNwFLAM+\ns4NDfjcinqh7vW/Q+fqB64F3Fu9Q6yp6zaDzXM0Qo5Bm5lPUBqPpiYj7gduB38vMXwMXUxuM5mbg\n7sHHSs0oav8HJUmSJElVZEuhJEmSJFWYSaEkSZIkVZhJoSRJkiRVmEmhJEmSJFWYSaEkSZIkVZhJ\noSRJkiRVmEmhJEmSJFWYSaEkSZIkVdj/DwroD9o/e9HaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461de4fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461d921668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAF3CAYAAAASFe6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSpAH2J7KpoxWFeNV+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461df04b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA38AAAF3CAYAAAAVe/LLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkyRJkqQB8DdQ1M3BVtSgOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461e1b8160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA38AAAF3CAYAAAAVe/LLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+hsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461d9216a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA38AAAF3CAYAAAAVe/LLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461dc30e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA38AAAF3CAYAAAAVe/LLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3X+cXHV99/3Xx4QGij9B3VLAgprLTlhvraaUC/e2uw1e\n4I8a2qu1jNBS7ylpCqxauJQfe7W2l51bqEXbpkXu0KXGSpeirRJ/oNC4o9cWEUFpCRmRKKhwBeLP\nQhSCWT73H3OCs0lIdrOZPTs7r+fjMY895zvnnHnPl7Cznznn+z2RmUiSJEmSFranlB1AkiRJktR5\nFn+SJEmS1AMs/iRJkiSpB1j8SZIkSVIPsPiTJEmSpB5g8SdJkiRJPcDiT5IkSZJ6gMWfJEmSJPUA\niz9JkiRJ6gEWf5IkSZLUAxaXHWA2nv3sZ+cxxxxTdgxpzv3whz/k0EMPLTuGNKduu+2272Tmc8rO\n0S0OxGekv2umsj+msj+msj92Z59M1an+mMnnY1cXf8cccwy33npr2TGkOddoNBgcHCw7hjSnIuIb\nZWfoJgfiM9LfNVPZH1PZH1PZH7uzT6bqVH/M5PPRyz4lSZIkqQdY/EmSJElSD7D4kyRJkqQeYPEn\nSZIkST3A4k+SJEmSeoDFnyRJkiT1AIs/SZIkSeoBFn+SJEmS1AMs/iRJkiSpB1j8SV1kbGyM/v5+\nVqxYQX9/P2NjY2VHkiRJUpdYXHYASdMzNjbGyMgIo6OjTE5OsmjRImq1GgDVarXkdJIkSZrvPPMn\ndYl6vc7o6ChDQ0MsXryYoaEhRkdHqdfrZUeTJElSF7D4k7pEs9lkYGBgStvAwADNZrOkRJIkSeom\nHb3sMyLuBR4GJoEdmbk8Ig4D/gk4BrgXeENmfr/Y/iKgVmz/5sz8dCfzSd2kUqkwMTHB0NDQE20T\nExNUKpUSU0layO64/z/53Qs/Matj3HvJaw9QGknSbM3Fmb+hzHxpZi4v1i8ENmTmUmBDsU5ELANO\nA44DTgEuj4hFc5BP6gojIyPUajXGx8fZsWMH4+Pj1Go1RkZGyo4mSZKkLlDGhC8rgcFieR3QAC4o\n2q/JzO3APRGxGTge+HwJGaV5Z+ekLsPDwzSbTSqVCvV63cleJEmSNC2dPvOXwL9GxG0Rsapo68vM\nLcXyA0BfsXwk8K22fe8r2iQVqtUqGzduZMOGDWzcuNHCT5IkSdPW6TN/A5l5f0Q8F7gxIr7S/mRm\nZkTkTA5YFJGrAPr6+mg0GgcsrNQttm3b5r99aZ6LiKuA1wFbM7N/l+fOB/4CeE5mfqdo2+O494h4\nOfB+4BDgk8BbMnNGn52SJEGHi7/MvL/4uTUiPkLrMs4HI+KIzNwSEUcAW4vN7weObtv9qKJt12Ou\nBdYCLF++PAcHBzv4DqT5qdFo4L99ad57P/A3wAfaGyPiaOC/Ad9sa2sf9/6ztK6a+S+ZOQm8DzgL\n+AKt4u8U4Po5yC9JWmA6dtlnRBwaEU/buUzrg24jsB44s9jsTOC6Ynk9cFpELImIY4GlwC2dyidJ\nUidl5ueA7+3hqfcCb6c1NGKnJ8a9Z+Y9wGbg+OJL0qdn5s3F2b4PAKd2OLokaYHq5Jm/PuAjEbHz\ndf4xMz8VEV8Ero2IGvAN4A0AmXlnRFwLbAJ2AOcU33hKkrQgRMRK4P7M/Pfi83GnI4Gb29Z3jnv/\ncbG8a7skSTPWseIvM78OvGQP7d8FVjzJPnWg3qlMkiSVJSJ+GriY1pUwnXqNAzouvu8QOP/FO2Z1\njIU0Ptnx1lPZH1PZH7uzT6aaD/1Rxq0eJEnqRS8AjgV2nvU7CvhSRBzPk497v79Y3rV9jw70uPg1\nV1/HZXfM7k+Fe0+fXYb5xPHWU9kfU9kfu7NPppoP/TEXN3mXJKnnZeYdmfnczDwmM4+hdQnnyzLz\nAZ5k3Htxa6SHIuKEaFWMv8NPxspLkjQjFn+SJHVARIwBnwdeFBH3FWPd9ygz7wR2jnv/FFPHvZ8N\n/B2tSWC+hjN9SpL2k5d9SpLUAZlZ3cfzx+yyvsdx75l5K9C/a7skSTPlmT9JkiRJ6gEWf5IkSZLU\nAyz+JEmSJKkHWPxJkiRJUg+w+JMkSZKkHmDxJ0mSJEk9wOJPkiRJknqAxZ8kSZIk9QCLP0mSJEnq\nARZ/kiRJktQDLP4kSZIkqQdY/EmSJElSD7D4kyRJkqQeYPEnSZIkST3A4k+SJEmSeoDFnyRJkiT1\nAIs/SZIkSeoBFn+SJEmS1AMs/iRJkiSpB1j8SZIkSVIPsPiTJEmSpB5g8SdJkiRJPcDiT5IkSZJ6\ngMWfJEmSJPUAiz9JkiRJ6gEWf5IkSZLUAyz+JEmSJKkHWPxJkiRJUg+w+JMkSZKkHmDxJ0mSJEk9\nwOJPkiRJknqAxZ8kSZIk9QCLP0mSJEnqARZ/kiRJktQDLP4kSeqAiLgqIrZGxMa2tndHxFci4j8i\n4iMR8cy25y6KiM0RcVdEnNzW/vKIuKN47q8jIub6vUiSFgaLP0mSOuP9wCm7tN0I9Gfm/wV8FbgI\nICKWAacBxxX7XB4Ri4p93gecBSwtHrseU5KkabH4kySpAzLzc8D3dmm7ITN3FKs3A0cVyyuBazJz\ne2beA2wGjo+II4CnZ+bNmZnAB4BT5+YdSJIWGos/SZLK8f8A1xfLRwLfanvuvqLtyGJ513ZJkmZs\ncdkBJEnqNRExAuwArj7Ax10FrALo6+uj0WjM6nh9h8D5L96x7w33YrYZ5pNt27YtqPczW/bHVPbH\n7uyTqeZDf1j8SZI0hyLid4HXASuKSzkB7geObtvsqKLtfn5yaWh7+x5l5lpgLcDy5ctzcHBwVlnX\nXH0dl90xuz8V7j19dhnmk0ajwWz7dCGxP6ayP3Znn0w1H/rDyz4lSZojEXEK8Hbg9Zn5o7an1gOn\nRcSSiDiW1sQut2TmFuChiDihmOXzd4Dr5jy4JGlB8MyfJEkdEBFjwCDw7Ii4D3gHrdk9lwA3Fnds\nuDkzV2fmnRFxLbCJ1uWg52TmZHGos2nNHHoIrTGC1yNJ0n6w+JO6yNjYGPV6nWazSaVSYWRkhGq1\nWnYsSXuQmXv6n3N0L9vXgfoe2m8F+g9gNElSj7L4k7rE2NgYIyMjjI6OMjk5yaJFi6jVagAWgJIk\nSdonx/xJXaJerzM6OsrQ0BCLFy9maGiI0dFR6vXdThRIkiRJu7H4k7pEs9lkYGBgStvAwADNZrOk\nRJIkSeomHS/+ImJRRHw5Ij5erB8WETdGxN3Fz2e1bXtRRGyOiLsi4uROZ5O6SaVSYWJiYkrbxMQE\nlUqlpESSJEnqJnNx5u8tQPupiQuBDZm5FNhQrBMRy4DTgOOAU4DLI2LRHOSTusLIyAi1Wo3x8XF2\n7NjB+Pg4tVqNkZGRsqNJkiSpC3R0wpeIOAp4La3Zy84rmlfSmvoaYB3QAC4o2q/JzO3APRGxGTge\n+HwnM0rdYuekLsPDw0/M9lmv153sRZIkSdPS6dk+/5LWzWyf1tbWV9y0FuABoK9YPhK4uW27+4o2\nSYVqtUq1WqXRaDA4OFh2HEmSJHWRjhV/EfE6YGtm3hYRg3vaJjMzInKGx10FrALo6+uj0WjMNqrU\ndbZt2+a/fUmSJM1IJ8/8vQJ4fUS8BjgYeHpEfBB4MCKOyMwtEXEEsLXY/n7g6Lb9jyrapsjMtcBa\ngOXLl6dnP9SLPPMnSZKkmerYhC+ZeVFmHpWZx9CayOUzmXkGsB44s9jsTOC6Ynk9cFpELImIY4Gl\nwC2dyidJkiRJvaTTY/725BLg2oioAd8A3gCQmXdGxLXAJmAHcE5mTpaQT5IkSZIWnDkp/jKzQWtW\nTzLzu8CKJ9muTmtmUEmSJEnSATQX9/mTJEmSJJXM4k+SJEmSeoDFnyRJkiT1AIs/SZIkSeoBFn+S\nJEmS1AMs/iRJkiSpB1j8SZIkSVIPsPiTJEmSpB5g8SdJkiRJPcDiT5IkSZJ6gMWfJEmSJPUAiz9J\nkiRJ6gEWf5IkSZLUAyz+JEmSJKkHWPxJkiRJUg+w+JMkSZKkHmDxJ0mSJEk9wOJPkiRJknqAxZ8k\nSZIk9QCLP0mSJEnqARZ/kiRJktQDLP4kSZIkqQdY/EmSJElSD7D4kySpAyLiqojYGhEb29oOi4gb\nI+Lu4uez2p67KCI2R8RdEXFyW/vLI+KO4rm/joiY6/ciSVoYLP4kSdqLiHhFRBxaLJ8REe+JiJ+b\nxq7vB07Zpe1CYENmLgU2FOtExDLgNOC4Yp/LI2JRsc/7gLOApcVj12NKkjQtFn+SJO3d+4AfRcRL\ngPOBrwEf2NdOmfk54Hu7NK8E1hXL64BT29qvycztmXkPsBk4PiKOAJ6emTdnZhaveyqSJO0Hiz9J\nkvZuR1F4rQT+JjP/Fnjafh6rLzO3FMsPAH3F8pHAt9q2u69oO7JY3rVdkqQZW1x2AEmS5rmHI+Ii\n4AzglRHxFOCg2R40MzMictbp2kTEKmAVQF9fH41GY1bH6zsEzn/xjlkdY7YZ5pNt27YtqPczW/bH\nVPbH7uyTqeZDf1j8SZK0d78FvBGoZeYDEfE84N37eawHI+KIzNxSXNK5tWi/Hzi6bbujirb7i+Vd\n2/coM9cCawGWL1+eg4OD+xmzZc3V13HZHbP7U+He02eXYT5pNBrMtk8XEvtjKvtjd/bJVPOhP7zs\nU5KkJ1FMujKWme/JzP8NkJnfzMx9jvl7EuuBM4vlM4Hr2tpPi4glEXEsrYldbikuEX0oIk4oZvn8\nnbZ9JEmaEc/8SZL0JDJzMiIej4hnZOZ/zmTfiBgDBoFnR8R9wDuAS4BrI6IGfAN4Q/E6d0bEtcAm\nYAdwTmZOFoc6m9bMoYcA1xcPSZJmzOJPkqS92wbcERE3Aj/c2ZiZb97bTplZfZKnVjzJ9nWgvof2\nW4H+aaeVJOlJWPxJkrR3/1I8JEnqahZ/kiTtRWaui4hDgOdl5l1l55EkaX854YskSXsREb8K3A58\nqlh/aUSsLzeVJEkzZ/EnSdLe/QlwPPADgMy8HXh+mYEkSdofFn+SJO3dj/cw0+fjpSSRJGkWHPMn\nSdLe3RkRbwQWRcRS4M3ATSVnkiRpxjzzJ3WRsbEx+vv7WbFiBf39/YyNjZUdSeoFw8BxwHZgDHgI\neGupiSRJ2g+e+ZO6xNjYGCMjI4yOjjI5OcmiRYuo1WoAVKtPdjsxSbOVmT8CRoqHJEldyzN/Upeo\n1+uMjo4yNDTE4sWLGRoaYnR0lHp9t3tCSzqAIuJjEbF+l8c/RMRbIuLgsvNJkjRdFn9Sl2g2mwwM\nDExpGxgYoNlslpRI6hlfB7YBVxaPh4CHgf9SrEuS1BW87FPqEpVKhYmJCYaGhp5om5iYoFKplJhK\n6gknZuYvtq1/LCK+mJm/GBF3lpZKkqQZ8syf1CVGRkao1WqMj4+zY8cOxsfHqdVqjIw4DEnqsKdG\nxPN2rhTLTy1WHysnkiRJM+eZP6lL7JzUZXh4mGazSaVSoV6vO9mL1HnnAxMR8TUggGOBsyPiUGBd\nqckkSZoBiz+pi1SrVarVKo1Gg8HBwbLjSD0hMz9Z3N/v54umuzLz0WL5L0uKJUnSjFn8SZK0by8H\njqH1ufmSiCAzP1BuJEmSZsbiT5KkvYiIfwBeANwOTBbNCVj8SZK6ihO+SF1kbGyM/v5+VqxYQX9/\nP2NjY2VHknrBcuAVmXl2Zg4XjzeXHUqSpJnq2Jm/4sa3nwOWFK/z4cx8R0QcBvwTrctn7gXekJnf\nL/a5CKjR+mb1zZn56U7lk7rN2NgYIyMjjI6OMjk5yaJFi6jVagBO+iJ11kbgZ4AtZQeRJGk2Onnm\nbzvwK5n5EuClwCkRcQJwIbAhM5cCG4p1ImIZcBpwHHAKcHlELOpgPqmr1Ot1RkdHGRoaYvHixQwN\nDTE6Okq9Xi87mrTQPRvYFBGfjoj1Ox9lh5IkaaY6duYvMxPYVqweVDwSWAkMFu3rgAZwQdF+TWZu\nB+6JiM3A8cDnO5VR6ibNZpOBgYEpbQMDAzSbzZISST3jT8oOIEnSgdDRMX8RsSgibge2Ajdm5heA\nvszceenMA0BfsXwk8K223e8r2iQBlUqFiYmJKW0TExNUKpWSEkm9ITM/S2uYwkHF8heBL5UaSpKk\n/dDR2T4zcxJ4aUQ8E/hIRPTv8nxGRM7kmBGxClgF0NfXR6PROFBxpXnt137t1zj99NN529vexrHH\nHst73/te3v3ud1Or1fz/QOqgiDiL1ufOYbRm/TwSuAJYUWYuSZJmak5u9ZCZP4iIcVpj+R6MiCMy\nc0tEHEHrrCDA/cDRbbsdVbTteqy1wFqA5cuXpze6Vq8YHBxk2bJl1Ot1ms0mlUqFyy67zMlepM47\nh9YwhC8AZObdEfHcciNJkjRzHbvsMyKeU5zxIyIOAV4FfAVYD5xZbHYmcF2xvB44LSKWRMSxwFLg\nlk7lk7pRtVpl48aNbNiwgY0bN1r4SXNje2Y+tnMlIhbTGsMuSVJXmXbxFxEnRsQbI+J3dj72scsR\nwHhE/Aet8RE3ZubHgUuAV0XE3cBJxTqZeSdwLbAJ+BRwTnHZqKSC9/mTSvHZiLgYOCQiXgV8CPhY\nyZkkSZqxaV32GRH/QGucw+207sEHrW89P/Bk+2TmfwC/sIf27/Ik4yQysw44b720B97nTyrNhbTu\nQXsH8PvAJ4G/KzWRJEn7Ybpj/pYDy4rbN0gqQft9/hqNBoODg4yOjjI8PGzxJ3VQZj4OXAlcGRGH\nAUf5eShJ6kbTvexzI/AznQwiae+8z59UjohoRMTTi8LvNlpF4HvLziVJ0kzttfiLiI9FxHrg2cCm\niPh0RKzf+ZibiJLA+/xJJXpGZj4E/Drwgcz8JbzNgySpC+3rss+/mJMUkvZpZGSEWq32xJi/8fFx\narUa9brDZKUOW1zcmugNwEjZYSRJ2l97Lf4y87MAxa0XtmTmo8X6IUBf5+NJ2mnnuL7h4eEn7vNX\nr9cd7yd13v8CPg1MZOYXI+L5wN0lZ5IkacamO+HLh4AT29Yni7ZfPOCJJD2parVKtVp9YsIXSZ2X\nmR+i9Zm3c/3rwH8vL5EkSftnuhO+LG6/wW2x/FOdiSRJ0vwREX9eTPhyUERsiIhvR8QZZeeSJGmm\nplv8fTsiXr9zJSJWAt/pTCRJkuaV/1ZM+PI64F7ghcDbSk0kSdJ+mG7xtxq4OCK+GRHfBC4AVnUu\nliRJ88bOIRKvBT6Umf852wNGxB9GxJ0RsTEixiLi4Ig4LCJujIi7i5/Patv+oojYHBF3RcTJs319\nSVJv2mfxFxFPAV6emScAy2jd7P3EzPxax9NJklS+j0fEV4CXAxsi4jnAo/t7sIg4EngzsDwz+4FF\nwGnAhcCGzFwKbCjWiYhlxfPHAacAl0fEolm8H0lSj9pn8ZeZjwNvL5a3Zea2jqeSJGmeyMwLaU16\ntjwzfwz8EFg5y8MuBg6JiMXATwP/pzjmuuL5dcCpxfJK4JrM3J6Z9wCbgeNn+fqSpB403dk+/zUi\n/gfwT7Q+9ADIzO91JJUkSfPLzwInRcTBbW0f2J8DZeb9EfEXwDeBR4AbMvOGiOjLzC3FZg/wk1sq\nHQnc3HaI+4o2SZJmZLrF328VP89pa0vg+Qc2jiRJ80tEvAMYpDX04ZPAq4EJ9rP4K8byrQSOBX4A\nfGjX2UMzMyMi9+PYqyjG5Pf19dFoNPYn4hP6DoHzX7xjVseYbYb5ZNu2bQvq/cyW/TGV/bE7+2Sq\n+dAf0yr+MvPYTgeRJGme+g3gJcCXM/NNEdEHfHAWxzsJuCczvw0QEf9C67LSByPiiMzcEhFHAFuL\n7e8Hjm7b/6iibTeZuRZYC7B8+fKc7f1A11x9HZfdMd3viffs3tNnl2E+8R6rU9kfU9kfu7NPppoP\n/TGt2T6Lexu9OSI+XDzOjYiDOh1OkqR54JFi/PuOiHg6raLs6H3sszffBE6IiJ+OiABWAE1gPXBm\nsc2ZwHXF8nrgtIhYEhHHAkuBW2bx+pKkHjXdr/PeBxwEXF6s/3bR9nudCCVJ0jxya0Q8E7gSuA3Y\nBnx+fw+WmV+IiA8DXwJ2AF+mdbbuqcC1EVEDvgG8odj+zoi4FthUbH9OZk7O4v1IknrUdIu/X8zM\nl7StfyYi/r0TgSRJmk8y8+xi8YqI+BTw9Mz8j1ke8x3AO3Zp3k7rLOCetq8D9dm8piRJ0y3+JiPi\nBTvv7RcRzwf81lGS1BMi4teBAVqTnU0Asyr+JEkqw3SLv7cB4xHxdSCAnwPe1LFUkiTNExFxOfBC\nYKxo+v2IOCkzz9nLbpIkzTvTne1zQ0QsBV5UNN2Vmds7F0uSpHnjV4BKZiZARKwD7iw3kiRJM7fX\n2T4jYmlEXBcRG4H3A9/NzP+w8JMk9ZDNwPPa1o8u2iRJ6ir7utXDVcDHgf9Oa1ayNR1PJEnS/PI0\noBkRjYgYpzXr5tMjYn1ErC85myRJ07avyz6flplXFsvvjogvdTqQJEnzzB+XHUCSpANhX8XfwRHx\nC7QmeQE4pH09My0GJUkLWmZ+tuwMkiQdCPu67HML8B7gsuLxQNv6X3Q2mqRdjY2N0d/fz4oVK+jv\n72dsbGzfO0mSJEns48xfZg7NVRBJezc2NsbIyAijo6NMTk6yaNEiarUaANVqteR0kiRJmu/2debv\nCRFxYkS8MSJ+Z+ejk8EkTVWv1xkdHWVoaIjFixczNDTE6Ogo9Xq97GjSghQRG4qfl5adRZKkA2Fa\n9/mLiH8AXgDcDkwWzQl8oEO5JO2i2WwyMDAwpW1gYIBms1lSImnBOyIiTgReHxHX8JPx74Dj3iVJ\n3WdaxR+wHFi28wa3kuZepVLhT//0T/noRz9Ks9mkUqlw6qmnUqlUyo4mLVR/DPwRcBSt8e7tktbN\n3yVJ6hrTLf42Aj9DawIYSSUYGhri0ksv5dJLL2XZsmVs2rSJCy64gNWrV5cdTVqQMvPDwIcj4o8y\n851l55EkabamW/w9G9gUEbcA23c2ZubrO5JK0m7Gx8e54IILuOqqq54483fBBRfw0Y9+tOxo0oKW\nme+MiNcDryyaGpn58TIzSZK0P6Zb/P1JJ0NI2rdms8mXv/xl/uzP/oxGo8Hg4CA//vGPede73lV2\nNGlBi4h3AccDVxdNb4mIEzPz4hJjSZI0Y9Od7fNW4H8XN7rdAjwDuKljqSTtplKpMDExMaVtYmLC\nMX9S570WeFVmXpWZVwGnAK8rOZMkSTM23eLvc8DBEXEkcAPw28D7OxVK0u5GRkao1WqMj4+zY8cO\nxsfHqdVqjIyMlB1N6gXPbFt+RmkpJEmahele9hmZ+aOIqAGXZ+afR8S/dzKYpKl23sh9eHj4iTF/\n9XrdG7xLnfcu4MsRMU7rdg+vBC4sN5IkSTM37eIvIv4rcDpQK9qmfYN4SQdGtVqlWq0+MeZPUudl\n5lhENIBfLJouyMwHSowkSdJ+mW7x9xbgIuAjmXlnRDwfGO9cLEmS5o/M3AKsLzuHJEmzMa3iLzM/\nR2vcHxHxLOCezHxzJ4NJkiRJkg6cvV66GRF/HBE/XywviYjPAF8DHoyIk+YioCRJkiRp9vY1bu+3\ngLuK5TOL7Z8D/DLw/3YwlyRJpYuIRRHxlbJzSJJ0IOyr+HssM7NYPhkYy8zJzGwy/fGCkiR1pcyc\nBO6KiOeVnUWSpNnaV/G3PSL6I+I5wBCte/zt9NOdiyVpT4aHhzn44IMZGhri4IMPZnh4uOxIUi94\nFnBnRGyIiPU7H2WHkiRppvZ19u6twIdpXer53sy8ByAiXgN8ucPZJLUZHh7miiuu4NJLL2XZsmVs\n2rSJCy64AIA1a9aUnE5a0P6o7ACSJB0Iez3zl5k30xrrd0pmvjMilkXEecVz3llamkNXXnkll156\nKeeddx4HH3ww5513HpdeeilXXnll2dGkBS0zPwvcCxxULH8R+FKpoSRJ2g/7mu3zHcBfAe+LiHcB\nfwMcClwYESNzkE9SYfv27axevXpK2+rVq9m+fXtJiaTeEBFn0boK5v8rmo4EPlpeIkmS9s++xvz9\nBvAK4JXAOcCpmflOWpO//FaHs0lqs2TJEq644oopbVdccQVLliwpKZHUM86h9Vn4EEBm3g08t9RE\nkiTth32N+dtRzHT2o4j4Wmbu/OB7JCIe73w8STudddZZT4zxW7ZsGe95z3u44IILdjsbKOmA256Z\nj0UEABGxGMi97yJJ0vyzr+LvsYj46cz8EfDynY0R8QzA4k+aQzsndbn44ovZvn07S5YsYfXq1U72\nInXeZyPiYuCQiHgVcDbwsZIzSZI0Y/u67POVReFHZrYXewfRmghG0hxas2YNjz76KOPj4zz66KMW\nftLcuBD4NnAH8PvAJ4H/WWoiSZL2w17P/GXmHmeSyMzvAN/Z274RcTTwAaCP1uUxazPzryLiMOCf\ngGNozZ72hsz8frHPRUANmATenJmfnsmbkSTpQMvMxyNiHfAFWp9nd2Wml31KkrrOvs78zcYO4PzM\nXAacAJwTEctofYO6ITOXAhuKdYrnTgOOA04BLo+IRR3MJ0nSPkXEa4GvAX9Na9brzRHx6nJTSZI0\ncx0r/jJzS2Z+qVh+GGjSmh57JbCu2GwdcGqxvBK4JjO3FzeT3wwc36l8kiRN02XAUGYOZuYvA0PA\ne2dzwIh4ZkR8OCK+EhHNiPivEXFYRNwYEXcXP5/Vtv1FEbE5Iu6KiJNn+X4kST2qk2f+nhARxwC/\nQOuSmb7M3FI89QCty0KhVRh+q223+4o2SZLK9HBmbm5b/zrw8CyP+VfApzLz54GX0PqC1CtjJEkd\nta/ZPmctIp4K/DPw1sx8aOdU2QCZmRExo3ETEbEKWAXQ19dHo9E4gGml+W3Dhg188IMf5Jvf/CbP\ne97zOOOMM1ixYkXZsaQFKSJ+vVi8NSI+CVxLa8zfbwJfnMVxn0Hr/rm/C5CZj9GaXXslMFhstg5o\nABfQdmUMcE9E7Lwy5vP7m0GS1Js6WvxFxEG0Cr+rM/NfiuYHI+KIzNwSEUcAW4v2+4Gj23Y/qmib\nIjPXAmsBli9fnoODg52KL80rY2NjXH311Vx11VVMTk6yaNEiarUay5Yto1qtlh1PWoh+tW35QeCX\ni+VvA4dImuSQAAAalElEQVTM4rjHFsf4+4h4CXAb8Bb2fmXMzW37e2WMJGm/RKcmLIvWKb51wPcy\n861t7e8GvpuZl0TEhcBhmfn2iDgO+Eda32b+LK1LXpYWN5nfo+XLl+ett97akfzSfNPf38+aNWsY\nGhqi0WgwODjI+Pg4w8PDbNy4sex4UsdFxG2ZubzsHLMVEctpFXOvyMwvRMRfAQ8Bw5n5zLbtvp+Z\nz4qIvwFuzswPFu2jwPWZ+eE9HLv96piXX3PNNbPKuvV7/8mDj8zqELz4yGfM7gDzyLZt23jqU59a\ndox5w/6Yyv7YnX0yVaf6Y2hoaNqfj5088/cK4LeBOyLi9qLtYuAS4NqIqAHfAN4AkJl3RsS1wCZa\nM4Wes7fCT+o1zWaTgYGBKW0DAwM0m82SEkm9ISKOBYZp3aLoic/NzHz9fh7yPuC+zPxCsf5hWuP7\nZnVlTJHpgF4ds+bq67jsjtn9qXDv6bPLMJ/s/OJNLfbHVPbH7uyTqeZDf3Ss+MvMCSCe5Ok9DlLK\nzDpQ71QmqZtVKhUmJiYYGhp6om1iYoJKpVJiKqknfBQYBT4GPD7bg2XmAxHxrYh4UWbeReszcVPx\nOJPWl6RnAtcVu6wH/jEi3kPrypilwC2zzSFJ6j0dn/BF0oExMjJCrVZjdHSUyclJxsfHqdVq1Ot+\nXyJ12KOZ+dcH+JjDwNUR8VO0Zg99E60ZuL0yRpLUMRZ/UpfYOanL8PAwzWaTSqVCvV53shep8/4q\nIt4B3ABs39m48162+yMzbwf2ND7DK2MkSR1j8Sd1kWq1SrVanRfXjEs95MW0xrD/Cj+57DOLdUmS\nusac3ORd0oExNjZGf38/K1asoL+/n7GxsbIjSb3gN4HnZ+YvZ+ZQ8bDwkyR1Hc/8SV1ibGyM1atX\n88gjj/D444/z1a9+ldWrVwN46afUWRuBZ/KT2TclSepKnvmTusS5557Ltm3buOSSS7j++uu55JJL\n2LZtG+eee27Z0aSF7pnAVyLi0xGxfuej7FCSJM2UZ/6kLvG9732PP//zP+e8886j0Whw3nnnMTk5\nydvf/vayo0kL3TvKDiBJ0oFg8Sd1kf7+/r2uSzrwMvOzZWeQJOlA8LJPqUssXryYM844g/HxcXbs\n2MH4+DhnnHEGixf7HY7USRHxcEQ8VDwejYjJiHio7FySJM2UfzVKXWL16tVcfvnlvPGNb2Tr1q08\n97nP5Qc/+AFnn3122dGkBS0zn7ZzOSICWAmcUF4iSZL2j2f+pC6xZs0azj77bL7//e/z+OOP8/3v\nf5+zzz6bNWvWlB1N6hnZ8lHg5LKzSJI0U575k7rIV7/6VR577DEAHnvsMb761a+WnEha+CLi19tW\nnwIsBx4tKY4kSfvNM39Slzj55JO54YYbWL16NR/72MdYvXo1N9xwAyef7AkIqcN+te1xMvAwrUs/\nJUnqKp75k7rEjTfeyB/8wR9w+eWX02g0uPzyywG44oorSk4mLWyZ+aayM0iSdCBY/EldIjN52cte\nRn9/P81mk0qlwlvf+lYys+xo0oIUEX+8l6czM985Z2EkSToALP6kLnLeeedx3XXXMTk5yaJFi1i5\n0ivPpA764R7aDgVqwOGAxZ8kqas45k/qEoceeigPP/wwH/rQh3j00Uf50Ic+xMMPP8yhhx5adjRp\nQcrMy3Y+gLXAIcCbgGuA55caTpKk/eCZP6lLPPLII5x00klcccUVvO997yMiOOmkk/jMZz5TdjRp\nwYqIw4DzgNOBdcDLMvP75aaSJGn/eOZP6hKVSoWLL76Yxx9/nPHxcR5//HEuvvhiKpVK2dGkBSki\n3g18kdbsni/OzD+x8JMkdTOLP6lLjIyMUKvVGB8fZ8eOHYyPj1Or1RgZGSk7mrRQnQ/8LPA/gf8T\nEQ8Vj4cj4qGSs0mSNGNe9il1iWq1yk033cSrX/1qtm/fzpIlSzjrrLOoVqtlR5MWpMz0C1JJ0oJi\n8Sd1ibGxMT7xiU9w/fXXPzHbZ61W48QTT7QAlCRJ0j75rabUJer1OqOjowwNDbF48WKGhoYYHR2l\nXq+XHU2SJEldwOJP6hLNZpOBgYEpbQMDAzSbzZISSZIkqZtY/EldolKpMDExMaVtYmLC2T4lSZI0\nLY75k7rEyMgIp556Ko888gg//vGPOeiggzjkkEO44ooryo4mSZKkLuCZP6lL3HTTTWzbto3DDz+c\npzzlKRx++OFs27aNm266qexokiRJ6gIWf1KXuPLKK6lWqxx++OEAHH744VSrVa688sqSk0mSJKkb\neNmn1CW2b9/OxMQEf//3f//ErR7e9KY3sX379rKjSZIkqQt45k/qEhHBa17zmim3enjNa15DRJQd\nTZIkSV3AM39Sl8hMrrzySl74wheybNky3vOe93DllVeSmWVHkyRJUhew+JO6xHHHHcfSpUu5+OKL\n2b59O0uWLOF1r3sdd999d9nRJEmS1AUs/qQuMTIywsjICNdff/0TY/5qtRr1er3saJIkSeoCFn9S\nl6hWq9x00028+tWvfuLM31lnnUW1Wi07miRJkrqAxZ/UJcbGxvjEJz6x25m/E0880QJQkiRJ++Rs\nn1KXqNfrjI6OTpntc3R01Ms+JUmSNC0Wf1KXaDabDAwMTGkbGBig2WyWlEjSbETEooj4ckR8vFg/\nLCJujIi7i5/Patv2oojYHBF3RcTJ5aWWJHUziz+pS1QqFSYmJqa0TUxMUKlUSkokaZbeArR/e3Mh\nsCEzlwIbinUiYhlwGnAccApweUQsmuOskqQFwOJP6hIjIyPUajXGx8fZsWMH4+Pj1Go1RkZGyo4m\naYYi4ijgtcDftTWvBNYVy+uAU9var8nM7Zl5D7AZOH6uskqSFg4nfJG6xM5JXYaHh2k2m1QqFer1\nupO9SN3pL4G3A09ra+vLzC3F8gNAX7F8JHBz23b3FW2SJM2IxZ/URarVKtVqlUajweDgYNlxJO2H\niHgdsDUzb4uIwT1tk5kZEbkfx14FrALo6+uj0WjMJip9h8D5L94xq2PMNsN8sm3btgX1fmbL/pjK\n/tidfTLVfOgPiz9JkubWK4DXR8RrgIOBp0fEB4EHI+KIzNwSEUcAW4vt7weObtv/qKJtN5m5FlgL\nsHz58pztl0Rrrr6Oy+6Y3Z8K954+uwzziV+8TWV/TGV/7M4+mWo+9Idj/iRJmkOZeVFmHpWZx9Ca\nyOUzmXkGsB44s9jsTOC6Ynk9cFpELImIY4GlwC1zHFuStAB45k+SpPnhEuDaiKgB3wDeAJCZd0bE\ntcAmYAdwTmZOlhdTktStLP4kSSpJZjaARrH8XWDFk2xXB+pzFkyStCB52ackSZIk9QCLP0mSJEnq\nARZ/kiRJktQDLP4kSZIkqQdY/EmSJElSD+hY8RcRV0XE1ojY2NZ2WETcGBF3Fz+f1fbcRRGxOSLu\nioiTO5VLkiRJknpRJ8/8vR84ZZe2C4ENmbkU2FCsExHLaN3o9rhin8sjYlEHs0mSJElST+lY8ZeZ\nnwO+t0vzSmBdsbwOOLWt/ZrM3J6Z9wCbgeM7lU2SJEmSes1cj/nry8wtxfIDQF+xfCTwrbbt7iva\nJLUZGxujv7+fFStW0N/fz9jYWNmRJEmS1CUWl/XCmZkRkTPdLyJWAasA+vr6aDQaBzqaNC9t2LCB\n0dFR3va2t3Hsscdyzz33cP7557Np0yZWrFhRdjxJkiTNc3Nd/D0YEUdk5paIOALYWrTfDxzdtt1R\nRdtuMnMtsBZg+fLlOTg42MG40vxx7rnncvXVVzM0NESj0eAP//APeelLX8rw8DDvfOc7y44nSZKk\neW6uL/tcD5xZLJ8JXNfWflpELImIY4GlwC1znE2a15rNJgMDA1PaBgYGaDabJSWSJElSN+nkrR7G\ngM8DL4qI+yKiBlwCvCoi7gZOKtbJzDuBa4FNwKeAczJzslPZpG5UqVSYmJiY0jYxMUGlUikpkSRJ\nkrpJxy77zMzqkzy1x8FJmVkH6p3KI3W7kZERarUao6OjTE5OMj4+Tq1Wo173fxtJkiTtW2kTvkia\nmWq19X3K8PAwzWaTSqVCvV5/ol2SJEnaG4s/qYtUq1Wq1SqNRgMnO5IkSdJMzPWEL5IkSZKkElj8\nSZIkSVIPsPiTJEmSpB5g8SdJkiRJPcDiT5IkSZJ6gMWfJEmSJPUAiz9JkiRJ6gEWf5IkSZLUAyz+\nJEmSJKkHWPxJkiRJUg+w+JMkSZKkHmDxJ0mSJEk9wOJPkiRJknqAxZ8kSZIk9QCLP0mSJEnqARZ/\nkiRJktQDLP4kSZIkqQdY/EmSJElSD7D4kyRJkqQeYPEnSdIcioijI2I8IjZFxJ0R8Zai/bCIuDEi\n7i5+Pqttn4siYnNE3BURJ5eXXpLUzSz+JEmaWzuA8zNzGXACcE5ELAMuBDZk5lJgQ7FO8dxpwHHA\nKcDlEbGolOSSpK5m8SdJ0hzKzC2Z+aVi+WGgCRwJrATWFZutA04tllcC12Tm9sy8B9gMHD+3qSVJ\nC4HFnyRJJYmIY4BfAL4A9GXmluKpB4C+YvlI4Fttu91XtEmSNCOLyw4gSVIvioinAv8MvDUzH4qI\nJ57LzIyI3I9jrgJWAfT19dFoNGaVse8QOP/FO2Z1jNlmmE+2bdu2oN7PbNkfU9kfu7NPppoP/WHx\nJ0nSHIuIg2gVfldn5r8UzQ9GxBGZuSUijgC2Fu33A0e37X5U0babzFwLrAVYvnx5Dg4Ozirnmquv\n47I7Zvenwr2nzy7DfNJoNJhtny4k9sdU9sfu7JOp5kN/eNmnJElzKFqn+EaBZma+p+2p9cCZxfKZ\nwHVt7adFxJKIOBZYCtwyV3klSQuHZ/4kSZpbrwB+G7gjIm4v2i4GLgGujYga8A3gDQCZeWdEXAts\nojVT6DmZOTn3sSVJ3c7iT5KkOZSZE0A8ydMrnmSfOlDvWChJUk/wsk9JkiRJ6gEWf5IkSZLUAyz+\nJEmSJKkHWPxJkiRJUg+w+JMkSZKkHmDxJ0mSJEk9wOJPkiRJknqAxZ/URcbGxujv72fFihX09/cz\nNjZWdiRJkiR1CW/yLnWJsbExRkZGGB0dZXJykkWLFlGr1QCoVqslp5MkSdJ855k/qUvU63VGR0cZ\nGhpi8eLFDA0NMTo6Sr1eLzuaJEmSuoDFn9Qlms0mAwMDU9oGBgZoNpslJZIkSVI3sfiTukSlUmFi\nYmJK28TEBJVKpaREkiRJ6iYWf1KXGBkZoVarMT4+zo4dOxgfH6dWqzEyMlJ2NEmSJHUBJ3yRusTO\nSV2Gh4dpNptUKhXq9bqTvUiSJGlaLP6kLlKtVqlWqzQaDQYHB8uOI0mSpC7iZZ+SJEmS1AMs/iRJ\nkiSpB1j8SZIkSVIPsPiTJEmSpB4w74q/iDglIu6KiM0RcWHZeSRJkiRpIZhXxV9ELAL+Fng1sAyo\nRsSyclNJkiRJUvebV8UfcDywOTO/npmPAdcAK0vOJEmSJEldb74Vf0cC32pbv69okyRJkiTNQtfd\n5D0iVgGrAPr6+mg0GuUGUs8b/sZwOS+8bm5fbs3PrZnbF5QkSdIBNd+Kv/uBo9vWjyranpCZa4G1\nAMuXL8/BwcE5CyftyR3cMeev2Wg08N++JEmSZmK+Xfb5RWBpRBwbET8FnAasLzmTJEmSJHW9eXXm\nLzN3RMS5wKeBRcBVmXlnybEkSZIkqevNq+IPIDM/CXyy7BySJEmStJDMt8s+JUmSJEkdYPEnSZIk\nST3A4k+SJEmSeoDFnyRJkiT1AIs/SZIkSeoBFn+SJEmS1AMs/iRJ6gIRcUpE3BURmyPiwrLzSJK6\nj8WfJEnzXEQsAv4WeDWwDKhGxLJyU0mSuo3FnyRJ89/xwObM/HpmPgZcA6wsOZMkqcssLjuAJEna\npyOBb7Wt3wf8UklZdAAcc+Enyo4wa+e/eAe/O8/ex72XvHbWx9jf/za79seByCIdaJGZZWfYbxHx\nbeAbZeeQSvBs4Dtlh5Dm2M9l5nPKDlGGiPgN4JTM/L1i/beBX8rMc3fZbhWwqlh9EXDXLF/a3zVT\n2R9T2R9T2R+7s0+m6lR/TPvzsavP/PXqHwFSRNyamcvLziFpztwPHN22flTRNkVmrgXWHqgX9XfN\nVPbHVPbHVPbH7uyTqeZDfzjmT5Kk+e+LwNKIODYifgo4DVhfciZJUpfp6jN/kiT1gszcERHnAp8G\nFgFXZeadJceSJHUZiz+pOx2wy7okdYfM/CTwyTl+WX/XTGV/TGV/TGV/7M4+mar0/ujqCV8kSZIk\nSdPjmD9JkiRJ6gEWf9IBFBEHR8QtEfHvEXFnRPxp0X5CRHwhIm6PiGZE/Ml+Hr8REXcVx/+3iHjR\nAcg8GBEfn+1xJC0sEXFK8ftmc0RcWHaeuRARV0XE1ojY2NZ2WETcGBF3Fz+f1fbcRUX/3BURJ5eT\nunMi4uiIGI+ITcVn2luK9p7sk718xvdkfwBExKKI+PLOvyN6uS8AIuLeiLij+Hvv1qJtXvWJxZ90\nYG0HfiUzXwK8FDglIk4A1gGrMvOlQD9w7Sxe4/Ti+OuAd093p4hYNIvXlNRDit8Xfwu8GlgGVCNi\nWbmp5sT7gVN2absQ2JCZS4ENxTpFf5wGHFfsc/kC/D27Azg/M5cBJwDnFO+7V/vkyT7je7U/AN4C\nNNvWe7kvdhrKzJe23dJhXvWJxZ90AGXLtmL1oOKRwHOBLcU2k5m5CSAifrn4duj24puzpxVn4hoR\n8eGI+EpEXB0RsYeX+xzwwuI4K4r97yi+uV5StN8bEZdGxJeA34yIF0bEvxbfWn4pIl5QHOup03g9\nSb3jeGBzZn49Mx8DrgFWlpyp4zLzc8D3dmleSevLNoqfp7a1X5OZ2zPzHmAzrX5bMDJzS2Z+qVh+\nmNYf+UfSo32yl8/4nuyPiDgKeC3wd23NPdkX+zCv+sTiTzrAiksgbge2Ajdm5heA9wJ3RcRHIuL3\nI+LgYvP/AZxTnBH8v4FHivZfAN5K6xv35wOv2MNL/SpwR3Gs9wO/lZkvpjWL7x+0bffdzHxZZl4D\nXA38bfGt5YkUBek0X09S7zgS+Fbb+n1FWy/qy8ydvysfAPqK5Z7qo4g4htZnxRfo4T55ks/4Xu2P\nvwTeDjze1tarfbFTAv8aEbdFxKqibV71icWfdIAVZ/ZeChwFHB8R/Zn5v4DlwA3AG4FPFZv/G/Ce\niHgz8MzM3FG035KZ92Xm48DtwDFtL3F18cHzClrF44uAezLzq8Xz64BXtm3/TwAR8TTgyMz8SJHz\n0cz80TReT5JE68wPrT/uekpEPBX4Z+CtmflQ+3O91id7+ozf5fme6I+IeB2wNTNve7JteqUvdjFQ\n/Pt4Na3LpNv/HpsXfWLxJ3VIZv4AGKcYP5KZX8vM9wErgJdExOGZeQnwe8AhwL9FxM8Xu29vO9Qk\nU+/JeXpxLfmpmdn+jdGT+eE0ttnb60nqPfcDR7etH1W09aIHI+IIgOLn1qK9J/ooIg6iVfhdnZn/\nUjT3dJ/Abp/xvdgfrwBeHxH30ros/Fci4oP0Zl88ITPvL35uBT5C6zLOedUnFn/SARQRz4mIZxbL\nhwCvAr4SEa9tG0e3lFaB9YOIeEFm3pGZlwJf5P9v735D/RzjOI6/P4bNn5XyJ+LB2NaWyEjC8neR\nEu2JkVkSkUh74AlP/HlkKR7JPCBLY0Yta7Ux+ZeRTYxjOErISiml/Nvajq8H96WO2dbZOH+2+/2q\nU7/7/l2/67rO1em+z+d3Xfd9w+zdVrx3g8C0JDPa9iLg7V0Ltes1tiaZ3/o3OcmR+9GepIPfJmBm\nklOTHE53U4LV49yn8bIauLm9vhl4Zdj+G9qx9FS6Y/vGcejfqGnnraeBL6rqsWFv9XJM9nSOp4fj\nUVX3VdUpVTWN7vjwRlXdRA/H4m9JjmqrrEhyFHAl8BkTbEz8dl/6f50ELGt3azoEWFlVa5KsAB5P\n8jvd3dMWVtVQksVJLqNbL78FWAtcsC8NVtW2JLcALyU5lO6ftqV7KL4IeCrJw8AO4Lr9+B0lHeSq\nameSu4FXgUnAM1W1ZZy7NeqSvABcChyXZCvwAPAIsDLJrcB3wAKAqtqSZCXwOd1x/a6qGhqXjo+e\nuXTnjYF2uQHA/fR3TPZ0jn+ffo7H7vT1bwO6a/lWte/6DwWer6p1STYxgcYk3dJTSZIkSdLBzGWf\nkiRJktQDhj9JkiRJ6gHDnyRJkiT1gOFPkiRJknrA8CdJkiRJPWD4kyRJ0qhLMiXJxiSfJNmS5KG2\n//wkHyTZnOSLJA/uZ/1vJRls9W9IMut/6POlSdb813qkicLn/EmSJGksbAcur6pfkxwGvJtkLbAM\nWFBVn7Rn6P2X0Lawqj5McjvwKHDtSD6UZNJB+Nw56V+c+ZMkSdKoq86vbfOw9lPACcAPrcxQVX0O\nkOSSNhu4OcnHSaa2mbi3kryc5Msky9Oeqr2Ld4AZrZ557fMDSZ5JMrnt/zbJkiQfAdclmZHk9TZz\n+FGS6a2uo0fQnnRAMPxJkiRpTCSZlGQz8COwvqo+AB4HBpOsSnJHkimt+L3AXVU1B7gI+KPtPxtY\nDJwOnAbM3U1T1wADra5ngeur6ky6VW93Div3U1WdU1UrgOXAE1V1FnAhLZCOsD3pgGD4kyRJ0pho\nM3tzgFOA85KcUVUPA+cCrwE3Auta8Q3AY0nuAY6pqp1t/8aq2lpVfwKbgWnDmljewuVcuvA4C/im\nqr5q7y8DLh5W/kWAJFOBk6tqVevntqr6fQTtSQcUw58kSZLGVFX9DLwJXNW2v66qJ4F5wFlJjq2q\nR4DbgCOADUlmt49vH1bVEP+8h8XCqppTVfOr6vsRdOW3EZTZW3vSAcXwJ0mSpFGX5Pgkx7TXRwBX\nAF8muXrYdXQz6QLWz0mmV9VAVS0BNgGzd1vx3g0C05LMaNuLgLd3LVRVvwBbk8xv/Zuc5Mj9aE+a\n0Ax/kiRJGgsnAW8m+ZQuzK2vqjV0gWywLdd8jm72bghYnOSzVn4HsHZfG6yqbcAtwEtJBoA/gaV7\nKL4IuKe19x5w4r62J010qarx7oMkSZIkaZQ58ydJkiRJPWD4kyRJkqQeMPxJkiRJUg8Y/iRJkiSp\nBwx/kiRJktQDhj9JkiRJ6gHDnyRJkiT1gOFPkiRJknrgLz+dMkqlKP4kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461de24438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4IAAAF3CAYAAADwyfXiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRMSASSIYERERERExYP4LUOLvzYLx27QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461e342f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5IAAAF3CAYAAADXZ3QKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSdJI/gW1ejAMeKaB6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461da3cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's make boxplots to visualise outliers in the continuous variables \n",
    "# and histograms to get an idea of the distribution\n",
    "\n",
    "for var in continuous:\n",
    "    plt.figure(figsize=(15,6))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    fig = data.boxplot(column=var)\n",
    "    fig.set_title('')\n",
    "    fig.set_ylabel(var)\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    fig = data[var].hist(bins=20)\n",
    "    fig.set_ylabel('Number of houses')\n",
    "    fig.set_xlabel(var)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of the continuous variables seem to contain outliers. In addition, the majority of the variables are not normally distributed. If we are planning to build linear regression, we might need to tackle these to improve the model performance. To tackle the 2 aspects together, I will do discretisation. And in particular, I will use trees to find the right buckets onto which I will divide the variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlies in discrete variables\n",
    "\n",
    "Let's calculate the percentage of houses for each  of the values that can take the discrete variables in the titanic dataset. I will call outliers, those values that are present in less than 1% of the houses. This is exactly the same as finding rare labels in categorical variables. Discrete variables, in essence can be pre-processed / engineered as if they were categorical. Keep this in mind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20     0.367123\n",
      "60     0.204795\n",
      "50     0.098630\n",
      "120    0.059589\n",
      "30     0.047260\n",
      "160    0.043151\n",
      "70     0.041096\n",
      "80     0.039726\n",
      "90     0.035616\n",
      "190    0.020548\n",
      "85     0.013699\n",
      "75     0.010959\n",
      "45     0.008219\n",
      "180    0.006849\n",
      "40     0.002740\n",
      "Name: MSSubClass, dtype: float64\n",
      "\n",
      "5     0.271918\n",
      "6     0.256164\n",
      "7     0.218493\n",
      "8     0.115068\n",
      "4     0.079452\n",
      "9     0.029452\n",
      "3     0.013699\n",
      "10    0.012329\n",
      "2     0.002055\n",
      "1     0.001370\n",
      "Name: OverallQual, dtype: float64\n",
      "\n",
      "5    0.562329\n",
      "6    0.172603\n",
      "7    0.140411\n",
      "8    0.049315\n",
      "4    0.039041\n",
      "3    0.017123\n",
      "9    0.015068\n",
      "2    0.003425\n",
      "1    0.000685\n",
      "Name: OverallCond, dtype: float64\n",
      "\n",
      "0    0.586301\n",
      "1    0.402740\n",
      "2    0.010274\n",
      "3    0.000685\n",
      "Name: BsmtFullBath, dtype: float64\n",
      "\n",
      "0    0.943836\n",
      "1    0.054795\n",
      "2    0.001370\n",
      "Name: BsmtHalfBath, dtype: float64\n",
      "\n",
      "2    0.526027\n",
      "1    0.445205\n",
      "3    0.022603\n",
      "0    0.006164\n",
      "Name: FullBath, dtype: float64\n",
      "\n",
      "0    0.625342\n",
      "1    0.366438\n",
      "2    0.008219\n",
      "Name: HalfBath, dtype: float64\n",
      "\n",
      "3    0.550685\n",
      "2    0.245205\n",
      "4    0.145890\n",
      "1    0.034247\n",
      "5    0.014384\n",
      "6    0.004795\n",
      "0    0.004110\n",
      "8    0.000685\n",
      "Name: BedroomAbvGr, dtype: float64\n",
      "\n",
      "1    0.953425\n",
      "2    0.044521\n",
      "3    0.001370\n",
      "0    0.000685\n",
      "Name: KitchenAbvGr, dtype: float64\n",
      "\n",
      "6     0.275342\n",
      "7     0.225342\n",
      "5     0.188356\n",
      "8     0.128082\n",
      "4     0.066438\n",
      "9     0.051370\n",
      "10    0.032192\n",
      "11    0.012329\n",
      "3     0.011644\n",
      "12    0.007534\n",
      "14    0.000685\n",
      "2     0.000685\n",
      "Name: TotRmsAbvGrd, dtype: float64\n",
      "\n",
      "0    0.472603\n",
      "1    0.445205\n",
      "2    0.078767\n",
      "3    0.003425\n",
      "Name: Fireplaces, dtype: float64\n",
      "\n",
      "2    0.564384\n",
      "1    0.252740\n",
      "3    0.123973\n",
      "0    0.055479\n",
      "4    0.003425\n",
      "Name: GarageCars, dtype: float64\n",
      "\n",
      "0      0.995205\n",
      "738    0.000685\n",
      "648    0.000685\n",
      "576    0.000685\n",
      "555    0.000685\n",
      "519    0.000685\n",
      "512    0.000685\n",
      "480    0.000685\n",
      "Name: PoolArea, dtype: float64\n",
      "\n",
      "6     0.173288\n",
      "7     0.160274\n",
      "5     0.139726\n",
      "4     0.096575\n",
      "8     0.083562\n",
      "3     0.072603\n",
      "10    0.060959\n",
      "11    0.054110\n",
      "9     0.043151\n",
      "12    0.040411\n",
      "1     0.039726\n",
      "2     0.035616\n",
      "Name: MoSold, dtype: float64\n",
      "\n",
      "2009    0.231507\n",
      "2007    0.225342\n",
      "2006    0.215068\n",
      "2008    0.208219\n",
      "2010    0.119863\n",
      "Name: YrSold, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# outlies in discrete variables\n",
    "for var in discrete:\n",
    "    print(data[var].value_counts() / np.float(len(data)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the discrete variables show values that are shared by a tiny proportion of houses in the dataset. For linear regression, this may not be a problem, but it most likely will be for tree methods.\n",
    "\n",
    "\n",
    "#### Number of labels: cardinality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning  contains  5  labels\n",
      "Street  contains  2  labels\n",
      "Alley  contains  3  labels\n",
      "LotShape  contains  4  labels\n",
      "LandContour  contains  4  labels\n",
      "Utilities  contains  2  labels\n",
      "LotConfig  contains  5  labels\n",
      "LandSlope  contains  3  labels\n",
      "Neighborhood  contains  25  labels\n",
      "Condition1  contains  9  labels\n",
      "Condition2  contains  8  labels\n",
      "BldgType  contains  5  labels\n",
      "HouseStyle  contains  8  labels\n",
      "RoofStyle  contains  6  labels\n",
      "RoofMatl  contains  8  labels\n",
      "Exterior1st  contains  15  labels\n",
      "Exterior2nd  contains  16  labels\n",
      "MasVnrType  contains  5  labels\n",
      "ExterQual  contains  4  labels\n",
      "ExterCond  contains  5  labels\n",
      "Foundation  contains  6  labels\n",
      "BsmtQual  contains  5  labels\n",
      "BsmtCond  contains  5  labels\n",
      "BsmtExposure  contains  5  labels\n",
      "BsmtFinType1  contains  7  labels\n",
      "BsmtFinType2  contains  7  labels\n",
      "Heating  contains  6  labels\n",
      "HeatingQC  contains  5  labels\n",
      "CentralAir  contains  2  labels\n",
      "Electrical  contains  6  labels\n",
      "KitchenQual  contains  4  labels\n",
      "Functional  contains  7  labels\n",
      "FireplaceQu  contains  6  labels\n",
      "GarageType  contains  7  labels\n",
      "GarageFinish  contains  4  labels\n",
      "GarageQual  contains  6  labels\n",
      "GarageCond  contains  6  labels\n",
      "PavedDrive  contains  3  labels\n",
      "PoolQC  contains  4  labels\n",
      "Fence  contains  5  labels\n",
      "MiscFeature  contains  5  labels\n",
      "SaleType  contains  9  labels\n",
      "SaleCondition  contains  6  labels\n"
     ]
    }
   ],
   "source": [
    "for var in categorical:\n",
    "    print(var, ' contains ', len(data[var].unique()), ' labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the variables, contain only a few labels. Then, we do not have to deal with high cardinality. That is good news!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separate train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1168, 81), (292, 81))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's separate into train and test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, data.SalePrice, test_size=0.2,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Engineering missing values in numerical variables (section 5)\n",
    "#### Continuous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage 0.181506849315\n",
      "MasVnrArea 0.00513698630137\n",
      "GarageYrBlt 0.0496575342466\n"
     ]
    }
   ],
   "source": [
    "# print variables with missing data\n",
    "for col in continuous:\n",
    "    if X_train[col].isnull().mean()>0:\n",
    "        print(col, X_train[col].isnull().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- LotFrontage and GarageYrBlt: create additional variable with NA + median imputation\n",
    "- CMasVnrArea: median imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add variable indicating missingness + median imputation\n",
    "for df in [X_train, X_test, submission]:\n",
    "    for var in ['LotFrontage', 'GarageYrBlt']:\n",
    "        df[var+'_NA'] = np.where(df[var].isnull(), 1, 0)\n",
    "        df[var].fillna(X_train[var].median(), inplace=True) \n",
    "\n",
    "for df in [X_train, X_test, submission]:\n",
    "    df.MasVnrArea.fillna(X_train.MasVnrArea.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discrete variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print variables with missing data\n",
    "for col in discrete:\n",
    "    if X_train[col].isnull().mean()>0:\n",
    "        print(col, X_train[col].isnull().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing data in the discrete variables. Good, then we don't have to engineer them.\n",
    "\n",
    "### Engineering Missing Data in categorical variables (section 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alley 0.939212328767\n",
      "MasVnrType 0.00513698630137\n",
      "BsmtQual 0.0239726027397\n",
      "BsmtCond 0.0239726027397\n",
      "BsmtExposure 0.0239726027397\n",
      "BsmtFinType1 0.0239726027397\n",
      "BsmtFinType2 0.0248287671233\n",
      "Electrical 0.000856164383562\n",
      "FireplaceQu 0.471746575342\n",
      "GarageType 0.0496575342466\n",
      "GarageFinish 0.0496575342466\n",
      "GarageQual 0.0496575342466\n",
      "GarageCond 0.0496575342466\n",
      "PoolQC 0.996575342466\n",
      "Fence 0.816780821918\n",
      "MiscFeature 0.958047945205\n"
     ]
    }
   ],
   "source": [
    "# print variables with missing data\n",
    "for col in categorical:\n",
    "    if X_train[col].isnull().mean()>0:\n",
    "        print(col, X_train[col].isnull().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will add a 'Missing' Label to all of them. If the missing data are rare, I will handle those together with rare labels in a subsequent engineering step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add label indicating 'Missing' to categorical variables\n",
    "\n",
    "for df in [X_train, X_test, submission]:\n",
    "    for var in categorical:\n",
    "        df[var].fillna('Missing', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# check absence of null values\n",
    "for var in X_train.columns:\n",
    "    if X_train[var].isnull().sum()>0:\n",
    "        print(var, X_train[var].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# check absence of null values\n",
    "for var in X_train.columns:\n",
    "    if X_test[var].isnull().sum()>0:\n",
    "        print(var, X_test[var].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BsmtFinSF1 1\n",
      "BsmtFinSF2 1\n",
      "BsmtUnfSF 1\n",
      "TotalBsmtSF 1\n",
      "BsmtFullBath 2\n",
      "BsmtHalfBath 2\n",
      "GarageCars 1\n",
      "GarageArea 1\n"
     ]
    }
   ],
   "source": [
    "# check absence of null values\n",
    "submission_vars = []\n",
    "for var in X_train.columns:\n",
    "    if var!='SalePrice' and submission[var].isnull().sum()>0:\n",
    "        print(var, submission[var].isnull().sum())\n",
    "        submission_vars.append(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fare in the submission dataset contains one null value, I will replace it by the median \n",
    "for var in submission_vars:\n",
    "    submission[var].fillna(X_train[var].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers in Numerical variables (15)\n",
    "\n",
    "In order to tackle outliers and skewed distributions at the same time, I suggested I would do discretisation. And in order to find the optimal buckets automatically, I would use decision trees to find the buckets for me."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tree_binariser(var):\n",
    "    score_ls = [] # here I will store the mse\n",
    "\n",
    "    for tree_depth in [1,2,3,4]:\n",
    "        # call the model\n",
    "        tree_model = DecisionTreeRegressor(max_depth=tree_depth)\n",
    "\n",
    "        # train the model using 3 fold cross validation\n",
    "        scores = cross_val_score(tree_model, X_train[var].to_frame(), y_train, cv=3, scoring='neg_mean_squared_error')\n",
    "        score_ls.append(np.mean(scores))\n",
    "\n",
    "    # find depth with smallest mse\n",
    "    depth = [1,2,3,4][np.argmax(score_ls)]\n",
    "    #print(score_ls, np.argmax(score_ls), depth)\n",
    "\n",
    "    # transform the variable using the tree\n",
    "    tree_model = DecisionTreeRegressor(max_depth=depth)\n",
    "    tree_model.fit(X_train[var].to_frame(), X_train.SalePrice)\n",
    "    X_train[var] = tree_model.predict(X_train[var].to_frame())\n",
    "    X_test[var] = tree_model.predict(X_test[var].to_frame())\n",
    "    submission[var] =  tree_model.predict(submission[var].to_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for var in continuous:\n",
    "    tree_binariser(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>MiscVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>244774.580000</td>\n",
       "      <td>215854.852814</td>\n",
       "      <td>254712.211111</td>\n",
       "      <td>231942.797101</td>\n",
       "      <td>234430.248963</td>\n",
       "      <td>162693.417544</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>283420.571429</td>\n",
       "      <td>271695.020833</td>\n",
       "      <td>325504.423077</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>199700.782235</td>\n",
       "      <td>251948.409639</td>\n",
       "      <td>274927.700000</td>\n",
       "      <td>157482.765625</td>\n",
       "      <td>221545.352423</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>217936.589286</td>\n",
       "      <td>182034.509821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>143409.933333</td>\n",
       "      <td>142922.191142</td>\n",
       "      <td>149604.171756</td>\n",
       "      <td>145548.741497</td>\n",
       "      <td>156067.643564</td>\n",
       "      <td>162693.417544</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>160164.021505</td>\n",
       "      <td>156267.219925</td>\n",
       "      <td>146426.575406</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>124728.476821</td>\n",
       "      <td>154389.937294</td>\n",
       "      <td>129813.178082</td>\n",
       "      <td>157482.765625</td>\n",
       "      <td>144015.982759</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>182034.509821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>181337.643952</td>\n",
       "      <td>215854.852814</td>\n",
       "      <td>131510.402477</td>\n",
       "      <td>231942.797101</td>\n",
       "      <td>156067.643564</td>\n",
       "      <td>196331.633333</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>184078.475884</td>\n",
       "      <td>156267.219925</td>\n",
       "      <td>146426.575406</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>124728.476821</td>\n",
       "      <td>138503.159184</td>\n",
       "      <td>176900.146617</td>\n",
       "      <td>157482.765625</td>\n",
       "      <td>144015.982759</td>\n",
       "      <td>142394.476190</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>182034.509821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>181337.643952</td>\n",
       "      <td>215854.852814</td>\n",
       "      <td>224159.198813</td>\n",
       "      <td>201707.040000</td>\n",
       "      <td>185895.184080</td>\n",
       "      <td>269568.187500</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>184078.475884</td>\n",
       "      <td>240148.982143</td>\n",
       "      <td>229591.006289</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>199700.782235</td>\n",
       "      <td>216624.805930</td>\n",
       "      <td>303610.295455</td>\n",
       "      <td>195914.158228</td>\n",
       "      <td>221545.352423</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>182034.509821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>244774.580000</td>\n",
       "      <td>215854.852814</td>\n",
       "      <td>224159.198813</td>\n",
       "      <td>201707.040000</td>\n",
       "      <td>185895.184080</td>\n",
       "      <td>162693.417544</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>233388.564103</td>\n",
       "      <td>240148.982143</td>\n",
       "      <td>229591.006289</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>199700.782235</td>\n",
       "      <td>216624.805930</td>\n",
       "      <td>303610.295455</td>\n",
       "      <td>219441.075075</td>\n",
       "      <td>221545.352423</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>182034.509821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LotFrontage        LotArea      YearBuilt   YearRemodAdd  \\\n",
       "618  244774.580000  215854.852814  254712.211111  231942.797101   \n",
       "870  143409.933333  142922.191142  149604.171756  145548.741497   \n",
       "92   181337.643952  215854.852814  131510.402477  231942.797101   \n",
       "817  181337.643952  215854.852814  224159.198813  201707.040000   \n",
       "302  244774.580000  215854.852814  224159.198813  201707.040000   \n",
       "\n",
       "        MasVnrArea     BsmtFinSF1     BsmtFinSF2      BsmtUnfSF  \\\n",
       "618  234430.248963  162693.417544  181949.470919  283420.571429   \n",
       "870  156067.643564  162693.417544  181949.470919  160164.021505   \n",
       "92   156067.643564  196331.633333  181949.470919  184078.475884   \n",
       "817  185895.184080  269568.187500  181949.470919  184078.475884   \n",
       "302  185895.184080  162693.417544  181949.470919  233388.564103   \n",
       "\n",
       "       TotalBsmtSF       1stFlrSF       2ndFlrSF   LowQualFinSF  \\\n",
       "618  271695.020833  325504.423077  170728.974203  180556.807198   \n",
       "870  156267.219925  146426.575406  170728.974203  180556.807198   \n",
       "92   156267.219925  146426.575406  170728.974203  180556.807198   \n",
       "817  240148.982143  229591.006289  170728.974203  180556.807198   \n",
       "302  240148.982143  229591.006289  170728.974203  180556.807198   \n",
       "\n",
       "         GrLivArea    GarageYrBlt     GarageArea     WoodDeckSF  \\\n",
       "618  199700.782235  251948.409639  274927.700000  157482.765625   \n",
       "870  124728.476821  154389.937294  129813.178082  157482.765625   \n",
       "92   124728.476821  138503.159184  176900.146617  157482.765625   \n",
       "817  199700.782235  216624.805930  303610.295455  195914.158228   \n",
       "302  199700.782235  216624.805930  303610.295455  219441.075075   \n",
       "\n",
       "       OpenPorchSF  EnclosedPorch      3SsnPorch    ScreenPorch        MiscVal  \n",
       "618  221545.352423  187096.857573  180269.684896  217936.589286  182034.509821  \n",
       "870  144015.982759  187096.857573  180269.684896  178939.159173  182034.509821  \n",
       "92   144015.982759  142394.476190  180269.684896  178939.159173  182034.509821  \n",
       "817  221545.352423  187096.857573  180269.684896  178939.159173  182034.509821  \n",
       "302  221545.352423  187096.857573  180269.684896  178939.159173  182034.509821  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[continuous].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage 4\n",
      "LotArea 4\n",
      "YearBuilt 8\n",
      "YearRemodAdd 16\n",
      "MasVnrArea 4\n",
      "BsmtFinSF1 4\n",
      "BsmtFinSF2 4\n",
      "BsmtUnfSF 8\n",
      "TotalBsmtSF 8\n",
      "1stFlrSF 8\n",
      "2ndFlrSF 14\n",
      "LowQualFinSF 2\n",
      "GrLivArea 8\n",
      "GarageYrBlt 8\n",
      "GarageArea 8\n",
      "WoodDeckSF 4\n",
      "OpenPorchSF 4\n",
      "EnclosedPorch 6\n",
      "3SsnPorch 4\n",
      "ScreenPorch 2\n",
      "MiscVal 2\n"
     ]
    }
   ],
   "source": [
    "for var in continuous:\n",
    "    print(var, len(X_train[var].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Engineering rare labels in categorical and discrete variables (section 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rare_imputation(variable):\n",
    "    # find frequent labels / discrete numbers\n",
    "    temp = X_train.groupby([variable])[variable].count()/np.float(len(X_train))\n",
    "    frequent_cat = [x for x in temp.loc[temp>0.03].index.values]\n",
    "    \n",
    "    X_train[variable] = np.where(X_train[variable].isin(frequent_cat), X_train[variable], 'Rare')\n",
    "    X_test[variable] = np.where(X_test[variable].isin(frequent_cat), X_test[variable], 'Rare')\n",
    "    submission[variable] = np.where(submission[variable].isin(frequent_cat), submission[variable], 'Rare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for var in ['BsmtFullBath', 'BsmtHalfBath', 'GarageCars']:\n",
    "    submission[var] = submission[var].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# find infrequent labels in categorical variables\n",
    "for var in categorical:\n",
    "    rare_imputation(var)\n",
    "    \n",
    "for var in discrete:\n",
    "    rare_imputation(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for var in X_train.columns:\n",
    "    if var!='SalePrice' and submission[var].isnull().sum()>0:\n",
    "        print(var, submission[var].isnull().sum())\n",
    "        submission_vars.append(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning RL      0.788527\n",
      "RM      0.148973\n",
      "FV      0.041952\n",
      "Rare    0.020548\n",
      "Name: MSZoning, dtype: float64\n",
      "\n",
      "Street Pave    0.995719\n",
      "Rare    0.004281\n",
      "Name: Street, dtype: float64\n",
      "\n",
      "Alley Missing    0.939212\n",
      "Grvl       0.031678\n",
      "Rare       0.029110\n",
      "Name: Alley, dtype: float64\n",
      "\n",
      "LotShape Reg     0.629281\n",
      "IR1     0.339041\n",
      "Rare    0.031678\n",
      "Name: LotShape, dtype: float64\n",
      "\n",
      "LandContour Lvl     0.902397\n",
      "Rare    0.054795\n",
      "Bnk     0.042808\n",
      "Name: LandContour, dtype: float64\n",
      "\n",
      "Utilities AllPub    0.999144\n",
      "Rare      0.000856\n",
      "Name: Utilities, dtype: float64\n",
      "\n",
      "LotConfig Inside     0.727740\n",
      "Corner     0.175514\n",
      "CulDSac    0.065068\n",
      "FR2        0.030822\n",
      "Rare       0.000856\n",
      "Name: LotConfig, dtype: float64\n",
      "\n",
      "LandSlope Gtl     0.941781\n",
      "Mod     0.047089\n",
      "Rare    0.011130\n",
      "Name: LandSlope, dtype: float64\n",
      "\n",
      "Neighborhood Rare       0.186644\n",
      "NAmes      0.151541\n",
      "CollgCr    0.099315\n",
      "OldTown    0.076199\n",
      "Edwards    0.068493\n",
      "Somerst    0.058219\n",
      "Sawyer     0.055651\n",
      "Gilbert    0.054795\n",
      "NridgHt    0.052226\n",
      "NWAmes     0.047945\n",
      "BrkSide    0.039384\n",
      "SawyerW    0.039384\n",
      "Mitchel    0.035959\n",
      "Crawfor    0.034247\n",
      "Name: Neighborhood, dtype: float64\n",
      "\n",
      "Condition1 Norm     0.870719\n",
      "Rare     0.076199\n",
      "Feedr    0.053082\n",
      "Name: Condition1, dtype: float64\n",
      "\n",
      "Condition2 Norm    0.993151\n",
      "Rare    0.006849\n",
      "Name: Condition2, dtype: float64\n",
      "\n",
      "BldgType 1Fam      0.839897\n",
      "TwnhsE    0.076199\n",
      "Rare      0.051370\n",
      "Duplex    0.032534\n",
      "Name: BldgType, dtype: float64\n",
      "\n",
      "HouseStyle 1Story    0.495719\n",
      "2Story    0.309932\n",
      "1.5Fin    0.105308\n",
      "Rare      0.046233\n",
      "SLvl      0.042808\n",
      "Name: HouseStyle, dtype: float64\n",
      "\n",
      "RoofStyle Gable    0.774829\n",
      "Hip      0.202055\n",
      "Rare     0.023116\n",
      "Name: RoofStyle, dtype: float64\n",
      "\n",
      "RoofMatl CompShg    0.981164\n",
      "Rare       0.018836\n",
      "Name: RoofMatl, dtype: float64\n",
      "\n",
      "Exterior1st VinylSd    0.352740\n",
      "Wd Sdng    0.149829\n",
      "HdBoard    0.146404\n",
      "MetalSd    0.140411\n",
      "Plywood    0.080479\n",
      "Rare       0.051370\n",
      "CemntBd    0.042808\n",
      "BrkFace    0.035959\n",
      "Name: Exterior1st, dtype: float64\n",
      "\n",
      "Exterior2nd VinylSd    0.343322\n",
      "Wd Sdng    0.144692\n",
      "HdBoard    0.136986\n",
      "MetalSd    0.136986\n",
      "Plywood    0.105308\n",
      "Rare       0.089897\n",
      "CmentBd    0.042808\n",
      "Name: Exterior2nd, dtype: float64\n",
      "\n",
      "MasVnrType None       0.600171\n",
      "BrkFace    0.289384\n",
      "Stone      0.095890\n",
      "Rare       0.014555\n",
      "Name: MasVnrType, dtype: float64\n",
      "\n",
      "ExterQual TA      0.624144\n",
      "Gd      0.332192\n",
      "Ex      0.033390\n",
      "Rare    0.010274\n",
      "Name: ExterQual, dtype: float64\n",
      "\n",
      "ExterCond TA      0.877568\n",
      "Gd      0.097603\n",
      "Rare    0.024829\n",
      "Name: ExterCond, dtype: float64\n",
      "\n",
      "Foundation PConc     0.438356\n",
      "CBlock    0.432363\n",
      "BrkTil    0.107021\n",
      "Rare      0.022260\n",
      "Name: Foundation, dtype: float64\n",
      "\n",
      "BsmtQual TA      0.452055\n",
      "Gd      0.419521\n",
      "Ex      0.080479\n",
      "Rare    0.047945\n",
      "Name: BsmtQual, dtype: float64\n",
      "\n",
      "BsmtCond TA      0.895548\n",
      "Gd      0.047089\n",
      "Fa      0.031678\n",
      "Rare    0.025685\n",
      "Name: BsmtCond, dtype: float64\n",
      "\n",
      "BsmtExposure No      0.657534\n",
      "Av      0.148973\n",
      "Gd      0.090753\n",
      "Mn      0.078767\n",
      "Rare    0.023973\n",
      "Name: BsmtExposure, dtype: float64\n",
      "\n",
      "BsmtFinType1 Unf     0.302226\n",
      "GLQ     0.282534\n",
      "ALQ     0.147260\n",
      "BLQ     0.105308\n",
      "Rec     0.090753\n",
      "LwQ     0.047945\n",
      "Rare    0.023973\n",
      "Name: BsmtFinType1, dtype: float64\n",
      "\n",
      "BsmtFinType2 Unf     0.858733\n",
      "Rare    0.071918\n",
      "LwQ     0.035959\n",
      "Rec     0.033390\n",
      "Name: BsmtFinType2, dtype: float64\n",
      "\n",
      "Heating GasA    0.978596\n",
      "Rare    0.021404\n",
      "Name: Heating, dtype: float64\n",
      "\n",
      "HeatingQC Ex      0.505993\n",
      "TA      0.292808\n",
      "Gd      0.167808\n",
      "Fa      0.032534\n",
      "Rare    0.000856\n",
      "Name: HeatingQC, dtype: float64\n",
      "\n",
      "CentralAir Y    0.933219\n",
      "N    0.066781\n",
      "Name: CentralAir, dtype: float64\n",
      "\n",
      "Electrical SBrkr    0.907534\n",
      "FuseA    0.070205\n",
      "Rare     0.022260\n",
      "Name: Electrical, dtype: float64\n",
      "\n",
      "KitchenQual TA      0.510274\n",
      "Gd      0.395548\n",
      "Ex      0.066781\n",
      "Rare    0.027397\n",
      "Name: KitchenQual, dtype: float64\n",
      "\n",
      "Functional Typ     0.931507\n",
      "Rare    0.068493\n",
      "Name: Functional, dtype: float64\n",
      "\n",
      "FireplaceQu Missing    0.471747\n",
      "Gd         0.252568\n",
      "TA         0.220034\n",
      "Rare       0.055651\n",
      "Name: FireplaceQu, dtype: float64\n",
      "\n",
      "GarageType Attchd     0.595890\n",
      "Detchd     0.269692\n",
      "BuiltIn    0.063356\n",
      "Missing    0.049658\n",
      "Rare       0.021404\n",
      "Name: GarageType, dtype: float64\n",
      "\n",
      "GarageFinish Unf        0.417808\n",
      "RFn        0.288527\n",
      "Fin        0.244007\n",
      "Missing    0.049658\n",
      "Name: GarageFinish, dtype: float64\n",
      "\n",
      "GarageQual TA         0.903253\n",
      "Missing    0.049658\n",
      "Fa         0.034247\n",
      "Rare       0.012842\n",
      "Name: GarageQual, dtype: float64\n",
      "\n",
      "GarageCond TA         0.910103\n",
      "Missing    0.049658\n",
      "Rare       0.040240\n",
      "Name: GarageCond, dtype: float64\n",
      "\n",
      "PavedDrive Y       0.913527\n",
      "N       0.067637\n",
      "Rare    0.018836\n",
      "Name: PavedDrive, dtype: float64\n",
      "\n",
      "PoolQC Missing    0.996575\n",
      "Rare       0.003425\n",
      "Name: PoolQC, dtype: float64\n",
      "\n",
      "Fence Missing    0.816781\n",
      "MnPrv      0.096747\n",
      "GdPrv      0.043664\n",
      "GdWo       0.036815\n",
      "Rare       0.005993\n",
      "Name: Fence, dtype: float64\n",
      "\n",
      "MiscFeature Missing    0.958048\n",
      "Shed       0.038527\n",
      "Rare       0.003425\n",
      "Name: MiscFeature, dtype: float64\n",
      "\n",
      "SaleType WD      0.872432\n",
      "New     0.082192\n",
      "Rare    0.045377\n",
      "Name: SaleType, dtype: float64\n",
      "\n",
      "SaleCondition Normal     0.829623\n",
      "Partial    0.083904\n",
      "Abnorml    0.067637\n",
      "Rare       0.018836\n",
      "Name: SaleCondition, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# let's check that it worked\n",
    "for var in categorical:\n",
    "    print(var, X_train[var].value_counts()/np.float(len(X_train)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for var in X_train.columns:\n",
    "    if var!='SalePrice' and submission[var].isnull().sum()>0:\n",
    "        print(var, submission[var].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode categorical and discrete variables (section 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def encode_categorical_variables(var, target):\n",
    "        # make label to price dictionary\n",
    "        ordered_labels = X_train.groupby([var])[target].mean().to_dict()\n",
    "        \n",
    "        # encode variables\n",
    "        X_train[var] = X_train[var].map(ordered_labels)\n",
    "        X_test[var] = X_test[var].map(ordered_labels)\n",
    "        submission[var] = submission[var].map(ordered_labels)\n",
    "\n",
    "# encode labels in categorical vars\n",
    "for var in categorical:\n",
    "    encode_categorical_variables(var, 'SalePrice')\n",
    "    \n",
    "# encode labels in discrete vars\n",
    "for var in discrete:\n",
    "    encode_categorical_variables(var, 'SalePrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for var in X_train.columns:\n",
    "    if var!='SalePrice' and submission[var].isnull().sum()>0:\n",
    "        print(var, submission[var].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>LotFrontage_NA</th>\n",
       "      <th>GarageYrBlt_NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>619</td>\n",
       "      <td>184802.941860</td>\n",
       "      <td>191112.542888</td>\n",
       "      <td>244774.580000</td>\n",
       "      <td>215854.852814</td>\n",
       "      <td>181009.158212</td>\n",
       "      <td>183253.524157</td>\n",
       "      <td>164502.708844</td>\n",
       "      <td>180625.987666</td>\n",
       "      <td>180846.010283</td>\n",
       "      <td>176705.292941</td>\n",
       "      <td>179862.495455</td>\n",
       "      <td>315236.573770</td>\n",
       "      <td>183885.405113</td>\n",
       "      <td>180922.02069</td>\n",
       "      <td>185725.392457</td>\n",
       "      <td>175539.977547</td>\n",
       "      <td>294733.390625</td>\n",
       "      <td>201734.593272</td>\n",
       "      <td>254712.211111</td>\n",
       "      <td>231942.797101</td>\n",
       "      <td>218227.529661</td>\n",
       "      <td>179436.582897</td>\n",
       "      <td>225259.960000</td>\n",
       "      <td>229469.960000</td>\n",
       "      <td>205957.144970</td>\n",
       "      <td>234430.248963</td>\n",
       "      <td>367868.461538</td>\n",
       "      <td>183378.131707</td>\n",
       "      <td>225829.750000</td>\n",
       "      <td>326039.521277</td>\n",
       "      <td>183242.204589</td>\n",
       "      <td>204721.362069</td>\n",
       "      <td>236226.845455</td>\n",
       "      <td>162693.417544</td>\n",
       "      <td>184622.364905</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>283420.571429</td>\n",
       "      <td>271695.020833</td>\n",
       "      <td>181709.111986</td>\n",
       "      <td>214993.06599</td>\n",
       "      <td>186158.813761</td>\n",
       "      <td>187028.395283</td>\n",
       "      <td>325504.423077</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>199700.782235</td>\n",
       "      <td>165594.030612</td>\n",
       "      <td>180971.578281</td>\n",
       "      <td>213551.978964</td>\n",
       "      <td>162845.735172</td>\n",
       "      <td>180756.652713</td>\n",
       "      <td>183217.146057</td>\n",
       "      <td>213800.800866</td>\n",
       "      <td>253864.338710</td>\n",
       "      <td>183816.463235</td>\n",
       "      <td>211926.204981</td>\n",
       "      <td>226208.555932</td>\n",
       "      <td>202098.170977</td>\n",
       "      <td>251948.409639</td>\n",
       "      <td>142893.495902</td>\n",
       "      <td>308849.062500</td>\n",
       "      <td>274927.700000</td>\n",
       "      <td>187095.717536</td>\n",
       "      <td>187740.811853</td>\n",
       "      <td>186730.340206</td>\n",
       "      <td>157482.765625</td>\n",
       "      <td>221545.352423</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>217936.589286</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>187571.216981</td>\n",
       "      <td>182078.329759</td>\n",
       "      <td>182034.509821</td>\n",
       "      <td>183938.518519</td>\n",
       "      <td>187530.407692</td>\n",
       "      <td>280094.270833</td>\n",
       "      <td>278236.510204</td>\n",
       "      <td>314813</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>184802.941860</td>\n",
       "      <td>191112.542888</td>\n",
       "      <td>143409.933333</td>\n",
       "      <td>142922.191142</td>\n",
       "      <td>181009.158212</td>\n",
       "      <td>183253.524157</td>\n",
       "      <td>164502.708844</td>\n",
       "      <td>180625.987666</td>\n",
       "      <td>180846.010283</td>\n",
       "      <td>176705.292941</td>\n",
       "      <td>179862.495455</td>\n",
       "      <td>145902.220339</td>\n",
       "      <td>170342.022472</td>\n",
       "      <td>180922.02069</td>\n",
       "      <td>185725.392457</td>\n",
       "      <td>175539.977547</td>\n",
       "      <td>133553.796238</td>\n",
       "      <td>201734.593272</td>\n",
       "      <td>149604.171756</td>\n",
       "      <td>145548.741497</td>\n",
       "      <td>218227.529661</td>\n",
       "      <td>179436.582897</td>\n",
       "      <td>147198.689024</td>\n",
       "      <td>147184.906250</td>\n",
       "      <td>155107.126961</td>\n",
       "      <td>156067.643564</td>\n",
       "      <td>145264.598080</td>\n",
       "      <td>183378.131707</td>\n",
       "      <td>149658.235644</td>\n",
       "      <td>141287.297348</td>\n",
       "      <td>183242.204589</td>\n",
       "      <td>166756.434896</td>\n",
       "      <td>169607.705382</td>\n",
       "      <td>162693.417544</td>\n",
       "      <td>184622.364905</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>160164.021505</td>\n",
       "      <td>156267.219925</td>\n",
       "      <td>181709.111986</td>\n",
       "      <td>155514.22449</td>\n",
       "      <td>106047.269231</td>\n",
       "      <td>187028.395283</td>\n",
       "      <td>146426.575406</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>124728.476821</td>\n",
       "      <td>165594.030612</td>\n",
       "      <td>180971.578281</td>\n",
       "      <td>134247.150289</td>\n",
       "      <td>162845.735172</td>\n",
       "      <td>157600.802817</td>\n",
       "      <td>183217.146057</td>\n",
       "      <td>140522.540268</td>\n",
       "      <td>140217.388889</td>\n",
       "      <td>183816.463235</td>\n",
       "      <td>141205.366606</td>\n",
       "      <td>141205.366606</td>\n",
       "      <td>135245.717460</td>\n",
       "      <td>154389.937294</td>\n",
       "      <td>142893.495902</td>\n",
       "      <td>127151.030100</td>\n",
       "      <td>129813.178082</td>\n",
       "      <td>187095.717536</td>\n",
       "      <td>187740.811853</td>\n",
       "      <td>186730.340206</td>\n",
       "      <td>157482.765625</td>\n",
       "      <td>144015.982759</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>187571.216981</td>\n",
       "      <td>182078.329759</td>\n",
       "      <td>182034.509821</td>\n",
       "      <td>185801.122222</td>\n",
       "      <td>177176.490421</td>\n",
       "      <td>172945.844946</td>\n",
       "      <td>174634.731682</td>\n",
       "      <td>109500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>96493.392857</td>\n",
       "      <td>191112.542888</td>\n",
       "      <td>181337.643952</td>\n",
       "      <td>215854.852814</td>\n",
       "      <td>181009.158212</td>\n",
       "      <td>121154.162162</td>\n",
       "      <td>205860.732323</td>\n",
       "      <td>214412.765625</td>\n",
       "      <td>180846.010283</td>\n",
       "      <td>176705.292941</td>\n",
       "      <td>179862.495455</td>\n",
       "      <td>216634.675000</td>\n",
       "      <td>183885.405113</td>\n",
       "      <td>180922.02069</td>\n",
       "      <td>185725.392457</td>\n",
       "      <td>175539.977547</td>\n",
       "      <td>133553.796238</td>\n",
       "      <td>161106.354430</td>\n",
       "      <td>131510.402477</td>\n",
       "      <td>231942.797101</td>\n",
       "      <td>170921.710497</td>\n",
       "      <td>179436.582897</td>\n",
       "      <td>145071.171429</td>\n",
       "      <td>145788.491124</td>\n",
       "      <td>155107.126961</td>\n",
       "      <td>156067.643564</td>\n",
       "      <td>145264.598080</td>\n",
       "      <td>176193.377193</td>\n",
       "      <td>134111.008000</td>\n",
       "      <td>203596.697959</td>\n",
       "      <td>183242.204589</td>\n",
       "      <td>166756.434896</td>\n",
       "      <td>161924.273256</td>\n",
       "      <td>196331.633333</td>\n",
       "      <td>184622.364905</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>184078.475884</td>\n",
       "      <td>156267.219925</td>\n",
       "      <td>181709.111986</td>\n",
       "      <td>214993.06599</td>\n",
       "      <td>186158.813761</td>\n",
       "      <td>187028.395283</td>\n",
       "      <td>146426.575406</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>124728.476821</td>\n",
       "      <td>202420.555085</td>\n",
       "      <td>180971.578281</td>\n",
       "      <td>134247.150289</td>\n",
       "      <td>162845.735172</td>\n",
       "      <td>157600.802817</td>\n",
       "      <td>183217.146057</td>\n",
       "      <td>140522.540268</td>\n",
       "      <td>140217.388889</td>\n",
       "      <td>183816.463235</td>\n",
       "      <td>141205.366606</td>\n",
       "      <td>141205.366606</td>\n",
       "      <td>135245.717460</td>\n",
       "      <td>138503.159184</td>\n",
       "      <td>142893.495902</td>\n",
       "      <td>184299.762406</td>\n",
       "      <td>176900.146617</td>\n",
       "      <td>187095.717536</td>\n",
       "      <td>187740.811853</td>\n",
       "      <td>186730.340206</td>\n",
       "      <td>157482.765625</td>\n",
       "      <td>144015.982759</td>\n",
       "      <td>142394.476190</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>187571.216981</td>\n",
       "      <td>182078.329759</td>\n",
       "      <td>182034.509821</td>\n",
       "      <td>185801.122222</td>\n",
       "      <td>177176.490421</td>\n",
       "      <td>172945.844946</td>\n",
       "      <td>174634.731682</td>\n",
       "      <td>163500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>818</td>\n",
       "      <td>184802.941860</td>\n",
       "      <td>191112.542888</td>\n",
       "      <td>181337.643952</td>\n",
       "      <td>215854.852814</td>\n",
       "      <td>181009.158212</td>\n",
       "      <td>183253.524157</td>\n",
       "      <td>205860.732323</td>\n",
       "      <td>180625.987666</td>\n",
       "      <td>180846.010283</td>\n",
       "      <td>221899.447368</td>\n",
       "      <td>179862.495455</td>\n",
       "      <td>157510.380952</td>\n",
       "      <td>183885.405113</td>\n",
       "      <td>180922.02069</td>\n",
       "      <td>185725.392457</td>\n",
       "      <td>175539.977547</td>\n",
       "      <td>276886.977778</td>\n",
       "      <td>201734.593272</td>\n",
       "      <td>224159.198813</td>\n",
       "      <td>201707.040000</td>\n",
       "      <td>218227.529661</td>\n",
       "      <td>179436.582897</td>\n",
       "      <td>225259.960000</td>\n",
       "      <td>229469.960000</td>\n",
       "      <td>205957.144970</td>\n",
       "      <td>185895.184080</td>\n",
       "      <td>231702.935567</td>\n",
       "      <td>183378.131707</td>\n",
       "      <td>225829.750000</td>\n",
       "      <td>203596.697959</td>\n",
       "      <td>183242.204589</td>\n",
       "      <td>166756.434896</td>\n",
       "      <td>236226.845455</td>\n",
       "      <td>269568.187500</td>\n",
       "      <td>184622.364905</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>184078.475884</td>\n",
       "      <td>240148.982143</td>\n",
       "      <td>181709.111986</td>\n",
       "      <td>214993.06599</td>\n",
       "      <td>186158.813761</td>\n",
       "      <td>187028.395283</td>\n",
       "      <td>229591.006289</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>199700.782235</td>\n",
       "      <td>202420.555085</td>\n",
       "      <td>180971.578281</td>\n",
       "      <td>213551.978964</td>\n",
       "      <td>162845.735172</td>\n",
       "      <td>180756.652713</td>\n",
       "      <td>183217.146057</td>\n",
       "      <td>213800.800866</td>\n",
       "      <td>195515.388679</td>\n",
       "      <td>183816.463235</td>\n",
       "      <td>239132.152174</td>\n",
       "      <td>226208.555932</td>\n",
       "      <td>202098.170977</td>\n",
       "      <td>216624.805930</td>\n",
       "      <td>203170.442136</td>\n",
       "      <td>308849.062500</td>\n",
       "      <td>303610.295455</td>\n",
       "      <td>187095.717536</td>\n",
       "      <td>187740.811853</td>\n",
       "      <td>186730.340206</td>\n",
       "      <td>195914.158228</td>\n",
       "      <td>221545.352423</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>187571.216981</td>\n",
       "      <td>182078.329759</td>\n",
       "      <td>182034.509821</td>\n",
       "      <td>183938.518519</td>\n",
       "      <td>179548.348178</td>\n",
       "      <td>172945.844946</td>\n",
       "      <td>174634.731682</td>\n",
       "      <td>271000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>303</td>\n",
       "      <td>184802.941860</td>\n",
       "      <td>191112.542888</td>\n",
       "      <td>244774.580000</td>\n",
       "      <td>215854.852814</td>\n",
       "      <td>181009.158212</td>\n",
       "      <td>183253.524157</td>\n",
       "      <td>205860.732323</td>\n",
       "      <td>180625.987666</td>\n",
       "      <td>180846.010283</td>\n",
       "      <td>182952.741463</td>\n",
       "      <td>179862.495455</td>\n",
       "      <td>202313.017241</td>\n",
       "      <td>183885.405113</td>\n",
       "      <td>180922.02069</td>\n",
       "      <td>185725.392457</td>\n",
       "      <td>175539.977547</td>\n",
       "      <td>208597.949020</td>\n",
       "      <td>201734.593272</td>\n",
       "      <td>224159.198813</td>\n",
       "      <td>201707.040000</td>\n",
       "      <td>170921.710497</td>\n",
       "      <td>179436.582897</td>\n",
       "      <td>216476.152913</td>\n",
       "      <td>217430.650873</td>\n",
       "      <td>205957.144970</td>\n",
       "      <td>185895.184080</td>\n",
       "      <td>231702.935567</td>\n",
       "      <td>183378.131707</td>\n",
       "      <td>225829.750000</td>\n",
       "      <td>203596.697959</td>\n",
       "      <td>183242.204589</td>\n",
       "      <td>166756.434896</td>\n",
       "      <td>169607.705382</td>\n",
       "      <td>162693.417544</td>\n",
       "      <td>184622.364905</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>233388.564103</td>\n",
       "      <td>240148.982143</td>\n",
       "      <td>181709.111986</td>\n",
       "      <td>214993.06599</td>\n",
       "      <td>186158.813761</td>\n",
       "      <td>187028.395283</td>\n",
       "      <td>229591.006289</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>199700.782235</td>\n",
       "      <td>165594.030612</td>\n",
       "      <td>180971.578281</td>\n",
       "      <td>213551.978964</td>\n",
       "      <td>162845.735172</td>\n",
       "      <td>180756.652713</td>\n",
       "      <td>183217.146057</td>\n",
       "      <td>213800.800866</td>\n",
       "      <td>160176.227129</td>\n",
       "      <td>183816.463235</td>\n",
       "      <td>211926.204981</td>\n",
       "      <td>206207.696498</td>\n",
       "      <td>202098.170977</td>\n",
       "      <td>216624.805930</td>\n",
       "      <td>203170.442136</td>\n",
       "      <td>308849.062500</td>\n",
       "      <td>303610.295455</td>\n",
       "      <td>187095.717536</td>\n",
       "      <td>187740.811853</td>\n",
       "      <td>186730.340206</td>\n",
       "      <td>219441.075075</td>\n",
       "      <td>221545.352423</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>180689.71134</td>\n",
       "      <td>187571.216981</td>\n",
       "      <td>182078.329759</td>\n",
       "      <td>182034.509821</td>\n",
       "      <td>185865.387755</td>\n",
       "      <td>182698.573123</td>\n",
       "      <td>172945.844946</td>\n",
       "      <td>174634.731682</td>\n",
       "      <td>205000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id     MSSubClass       MSZoning    LotFrontage        LotArea  \\\n",
       "618  619  184802.941860  191112.542888  244774.580000  215854.852814   \n",
       "870  871  184802.941860  191112.542888  143409.933333  142922.191142   \n",
       "92    93   96493.392857  191112.542888  181337.643952  215854.852814   \n",
       "817  818  184802.941860  191112.542888  181337.643952  215854.852814   \n",
       "302  303  184802.941860  191112.542888  244774.580000  215854.852814   \n",
       "\n",
       "            Street          Alley       LotShape    LandContour  \\\n",
       "618  181009.158212  183253.524157  164502.708844  180625.987666   \n",
       "870  181009.158212  183253.524157  164502.708844  180625.987666   \n",
       "92   181009.158212  121154.162162  205860.732323  214412.765625   \n",
       "817  181009.158212  183253.524157  205860.732323  180625.987666   \n",
       "302  181009.158212  183253.524157  205860.732323  180625.987666   \n",
       "\n",
       "         Utilities      LotConfig      LandSlope   Neighborhood  \\\n",
       "618  180846.010283  176705.292941  179862.495455  315236.573770   \n",
       "870  180846.010283  176705.292941  179862.495455  145902.220339   \n",
       "92   180846.010283  176705.292941  179862.495455  216634.675000   \n",
       "817  180846.010283  221899.447368  179862.495455  157510.380952   \n",
       "302  180846.010283  182952.741463  179862.495455  202313.017241   \n",
       "\n",
       "        Condition1    Condition2       BldgType     HouseStyle    OverallQual  \\\n",
       "618  183885.405113  180922.02069  185725.392457  175539.977547  294733.390625   \n",
       "870  170342.022472  180922.02069  185725.392457  175539.977547  133553.796238   \n",
       "92   183885.405113  180922.02069  185725.392457  175539.977547  133553.796238   \n",
       "817  183885.405113  180922.02069  185725.392457  175539.977547  276886.977778   \n",
       "302  183885.405113  180922.02069  185725.392457  175539.977547  208597.949020   \n",
       "\n",
       "       OverallCond      YearBuilt   YearRemodAdd      RoofStyle  \\\n",
       "618  201734.593272  254712.211111  231942.797101  218227.529661   \n",
       "870  201734.593272  149604.171756  145548.741497  218227.529661   \n",
       "92   161106.354430  131510.402477  231942.797101  170921.710497   \n",
       "817  201734.593272  224159.198813  201707.040000  218227.529661   \n",
       "302  201734.593272  224159.198813  201707.040000  170921.710497   \n",
       "\n",
       "          RoofMatl    Exterior1st    Exterior2nd     MasVnrType  \\\n",
       "618  179436.582897  225259.960000  229469.960000  205957.144970   \n",
       "870  179436.582897  147198.689024  147184.906250  155107.126961   \n",
       "92   179436.582897  145071.171429  145788.491124  155107.126961   \n",
       "817  179436.582897  225259.960000  229469.960000  205957.144970   \n",
       "302  179436.582897  216476.152913  217430.650873  205957.144970   \n",
       "\n",
       "        MasVnrArea      ExterQual      ExterCond     Foundation  \\\n",
       "618  234430.248963  367868.461538  183378.131707  225829.750000   \n",
       "870  156067.643564  145264.598080  183378.131707  149658.235644   \n",
       "92   156067.643564  145264.598080  176193.377193  134111.008000   \n",
       "817  185895.184080  231702.935567  183378.131707  225829.750000   \n",
       "302  185895.184080  231702.935567  183378.131707  225829.750000   \n",
       "\n",
       "          BsmtQual       BsmtCond   BsmtExposure   BsmtFinType1  \\\n",
       "618  326039.521277  183242.204589  204721.362069  236226.845455   \n",
       "870  141287.297348  183242.204589  166756.434896  169607.705382   \n",
       "92   203596.697959  183242.204589  166756.434896  161924.273256   \n",
       "817  203596.697959  183242.204589  166756.434896  236226.845455   \n",
       "302  203596.697959  183242.204589  166756.434896  169607.705382   \n",
       "\n",
       "        BsmtFinSF1   BsmtFinType2     BsmtFinSF2      BsmtUnfSF  \\\n",
       "618  162693.417544  184622.364905  181949.470919  283420.571429   \n",
       "870  162693.417544  184622.364905  181949.470919  160164.021505   \n",
       "92   196331.633333  184622.364905  181949.470919  184078.475884   \n",
       "817  269568.187500  184622.364905  181949.470919  184078.475884   \n",
       "302  162693.417544  184622.364905  181949.470919  233388.564103   \n",
       "\n",
       "       TotalBsmtSF        Heating     HeatingQC     CentralAir     Electrical  \\\n",
       "618  271695.020833  181709.111986  214993.06599  186158.813761  187028.395283   \n",
       "870  156267.219925  181709.111986  155514.22449  106047.269231  187028.395283   \n",
       "92   156267.219925  181709.111986  214993.06599  186158.813761  187028.395283   \n",
       "817  240148.982143  181709.111986  214993.06599  186158.813761  187028.395283   \n",
       "302  240148.982143  181709.111986  214993.06599  186158.813761  187028.395283   \n",
       "\n",
       "          1stFlrSF       2ndFlrSF   LowQualFinSF      GrLivArea  \\\n",
       "618  325504.423077  170728.974203  180556.807198  199700.782235   \n",
       "870  146426.575406  170728.974203  180556.807198  124728.476821   \n",
       "92   146426.575406  170728.974203  180556.807198  124728.476821   \n",
       "817  229591.006289  170728.974203  180556.807198  199700.782235   \n",
       "302  229591.006289  170728.974203  180556.807198  199700.782235   \n",
       "\n",
       "      BsmtFullBath   BsmtHalfBath       FullBath       HalfBath  \\\n",
       "618  165594.030612  180971.578281  213551.978964  162845.735172   \n",
       "870  165594.030612  180971.578281  134247.150289  162845.735172   \n",
       "92   202420.555085  180971.578281  134247.150289  162845.735172   \n",
       "817  202420.555085  180971.578281  213551.978964  162845.735172   \n",
       "302  165594.030612  180971.578281  213551.978964  162845.735172   \n",
       "\n",
       "      BedroomAbvGr   KitchenAbvGr    KitchenQual   TotRmsAbvGrd  \\\n",
       "618  180756.652713  183217.146057  213800.800866  253864.338710   \n",
       "870  157600.802817  183217.146057  140522.540268  140217.388889   \n",
       "92   157600.802817  183217.146057  140522.540268  140217.388889   \n",
       "817  180756.652713  183217.146057  213800.800866  195515.388679   \n",
       "302  180756.652713  183217.146057  213800.800866  160176.227129   \n",
       "\n",
       "        Functional     Fireplaces    FireplaceQu     GarageType  \\\n",
       "618  183816.463235  211926.204981  226208.555932  202098.170977   \n",
       "870  183816.463235  141205.366606  141205.366606  135245.717460   \n",
       "92   183816.463235  141205.366606  141205.366606  135245.717460   \n",
       "817  183816.463235  239132.152174  226208.555932  202098.170977   \n",
       "302  183816.463235  211926.204981  206207.696498  202098.170977   \n",
       "\n",
       "       GarageYrBlt   GarageFinish     GarageCars     GarageArea  \\\n",
       "618  251948.409639  142893.495902  308849.062500  274927.700000   \n",
       "870  154389.937294  142893.495902  127151.030100  129813.178082   \n",
       "92   138503.159184  142893.495902  184299.762406  176900.146617   \n",
       "817  216624.805930  203170.442136  308849.062500  303610.295455   \n",
       "302  216624.805930  203170.442136  308849.062500  303610.295455   \n",
       "\n",
       "        GarageQual     GarageCond     PavedDrive     WoodDeckSF  \\\n",
       "618  187095.717536  187740.811853  186730.340206  157482.765625   \n",
       "870  187095.717536  187740.811853  186730.340206  157482.765625   \n",
       "92   187095.717536  187740.811853  186730.340206  157482.765625   \n",
       "817  187095.717536  187740.811853  186730.340206  195914.158228   \n",
       "302  187095.717536  187740.811853  186730.340206  219441.075075   \n",
       "\n",
       "       OpenPorchSF  EnclosedPorch      3SsnPorch    ScreenPorch      PoolArea  \\\n",
       "618  221545.352423  187096.857573  180269.684896  217936.589286  180689.71134   \n",
       "870  144015.982759  187096.857573  180269.684896  178939.159173  180689.71134   \n",
       "92   144015.982759  142394.476190  180269.684896  178939.159173  180689.71134   \n",
       "817  221545.352423  187096.857573  180269.684896  178939.159173  180689.71134   \n",
       "302  221545.352423  187096.857573  180269.684896  178939.159173  180689.71134   \n",
       "\n",
       "           PoolQC          Fence    MiscFeature        MiscVal         MoSold  \\\n",
       "618  180689.71134  187571.216981  182078.329759  182034.509821  183938.518519   \n",
       "870  180689.71134  187571.216981  182078.329759  182034.509821  185801.122222   \n",
       "92   180689.71134  187571.216981  182078.329759  182034.509821  185801.122222   \n",
       "817  180689.71134  187571.216981  182078.329759  182034.509821  183938.518519   \n",
       "302  180689.71134  187571.216981  182078.329759  182034.509821  185865.387755   \n",
       "\n",
       "            YrSold       SaleType  SaleCondition  SalePrice  LotFrontage_NA  \\\n",
       "618  187530.407692  280094.270833  278236.510204     314813               0   \n",
       "870  177176.490421  172945.844946  174634.731682     109500               0   \n",
       "92   177176.490421  172945.844946  174634.731682     163500               0   \n",
       "817  179548.348178  172945.844946  174634.731682     271000               1   \n",
       "302  182698.573123  172945.844946  174634.731682     205000               0   \n",
       "\n",
       "     GarageYrBlt_NA  \n",
       "618               0  \n",
       "870               0  \n",
       "92                0  \n",
       "817               0  \n",
       "302               0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's inspect the dataset\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the labels have now been replaced by the mean house price.\n",
    "\n",
    "### Feature scaling (section 13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>LotFrontage_NA</th>\n",
       "      <th>GarageYrBlt_NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>738.685788</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>180808.898973</td>\n",
       "      <td>0.181507</td>\n",
       "      <td>0.049658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.609683</td>\n",
       "      <td>38480.990758</td>\n",
       "      <td>25720.477372</td>\n",
       "      <td>33736.046396</td>\n",
       "      <td>37115.680039</td>\n",
       "      <td>3055.507893</td>\n",
       "      <td>11140.998208</td>\n",
       "      <td>21889.220846</td>\n",
       "      <td>11298.306442</td>\n",
       "      <td>1268.316692</td>\n",
       "      <td>11199.613349</td>\n",
       "      <td>3929.532340</td>\n",
       "      <td>45860.552169</td>\n",
       "      <td>9128.523689</td>\n",
       "      <td>1362.749348</td>\n",
       "      <td>14168.223861</td>\n",
       "      <td>22145.611412</td>\n",
       "      <td>55164.787624</td>\n",
       "      <td>24583.702981</td>\n",
       "      <td>49618.871670</td>\n",
       "      <td>43963.319552</td>\n",
       "      <td>18957.714577</td>\n",
       "      <td>9908.801138</td>\n",
       "      <td>31675.032788</td>\n",
       "      <td>31237.129369</td>\n",
       "      <td>35361.242793</td>\n",
       "      <td>39197.954278</td>\n",
       "      <td>53953.879151</td>\n",
       "      <td>11793.776051</td>\n",
       "      <td>40189.096743</td>\n",
       "      <td>53501.729438</td>\n",
       "      <td>18860.218446</td>\n",
       "      <td>29723.844614</td>\n",
       "      <td>36637.286166</td>\n",
       "      <td>40718.777833</td>\n",
       "      <td>10001.037180</td>\n",
       "      <td>9020.532512</td>\n",
       "      <td>27800.843566</td>\n",
       "      <td>53928.565770</td>\n",
       "      <td>6089.538646</td>\n",
       "      <td>35198.761059</td>\n",
       "      <td>20007.775506</td>\n",
       "      <td>19586.684249</td>\n",
       "      <td>51714.876682</td>\n",
       "      <td>43508.967190</td>\n",
       "      <td>8615.492267</td>\n",
       "      <td>57496.683423</td>\n",
       "      <td>18160.012243</td>\n",
       "      <td>1637.443705</td>\n",
       "      <td>44259.676776</td>\n",
       "      <td>23230.103527</td>\n",
       "      <td>18963.116711</td>\n",
       "      <td>11184.030580</td>\n",
       "      <td>52529.934030</td>\n",
       "      <td>39490.667509</td>\n",
       "      <td>11096.100031</td>\n",
       "      <td>38140.877100</td>\n",
       "      <td>38095.625026</td>\n",
       "      <td>38765.020398</td>\n",
       "      <td>47983.619169</td>\n",
       "      <td>42784.463581</td>\n",
       "      <td>55753.813144</td>\n",
       "      <td>54314.602977</td>\n",
       "      <td>22515.160301</td>\n",
       "      <td>22447.174795</td>\n",
       "      <td>19383.388123</td>\n",
       "      <td>27523.829396</td>\n",
       "      <td>35638.561525</td>\n",
       "      <td>18039.742483</td>\n",
       "      <td>6238.054701</td>\n",
       "      <td>8335.382737</td>\n",
       "      <td>2034.059631</td>\n",
       "      <td>2034.059631</td>\n",
       "      <td>16127.286579</td>\n",
       "      <td>6184.091203</td>\n",
       "      <td>5922.798839</td>\n",
       "      <td>7697.828418</td>\n",
       "      <td>4423.740972</td>\n",
       "      <td>30036.572861</td>\n",
       "      <td>30678.760855</td>\n",
       "      <td>78499.911304</td>\n",
       "      <td>0.385603</td>\n",
       "      <td>0.217329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>96493.392857</td>\n",
       "      <td>107592.250000</td>\n",
       "      <td>143409.933333</td>\n",
       "      <td>142922.191142</td>\n",
       "      <td>134228.600000</td>\n",
       "      <td>121154.162162</td>\n",
       "      <td>164502.708844</td>\n",
       "      <td>141651.720000</td>\n",
       "      <td>137500.000000</td>\n",
       "      <td>128000.000000</td>\n",
       "      <td>179862.495455</td>\n",
       "      <td>124453.375000</td>\n",
       "      <td>145369.306452</td>\n",
       "      <td>164406.250000</td>\n",
       "      <td>134357.500000</td>\n",
       "      <td>142481.121951</td>\n",
       "      <td>110298.527473</td>\n",
       "      <td>123138.166667</td>\n",
       "      <td>131510.402477</td>\n",
       "      <td>101331.000000</td>\n",
       "      <td>170921.710497</td>\n",
       "      <td>179436.582897</td>\n",
       "      <td>145071.171429</td>\n",
       "      <td>145788.491124</td>\n",
       "      <td>155107.126961</td>\n",
       "      <td>156067.643564</td>\n",
       "      <td>86607.750000</td>\n",
       "      <td>108143.586207</td>\n",
       "      <td>123795.269231</td>\n",
       "      <td>110267.928571</td>\n",
       "      <td>101338.466667</td>\n",
       "      <td>104005.500000</td>\n",
       "      <td>104005.500000</td>\n",
       "      <td>162693.417544</td>\n",
       "      <td>148912.940476</td>\n",
       "      <td>152023.418919</td>\n",
       "      <td>84500.000000</td>\n",
       "      <td>118811.691358</td>\n",
       "      <td>139651.160000</td>\n",
       "      <td>87000.000000</td>\n",
       "      <td>106047.269231</td>\n",
       "      <td>108605.269231</td>\n",
       "      <td>114588.758621</td>\n",
       "      <td>116307.896104</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>87380.861111</td>\n",
       "      <td>165594.030612</td>\n",
       "      <td>127500.000000</td>\n",
       "      <td>134247.150289</td>\n",
       "      <td>162845.735172</td>\n",
       "      <td>157600.802817</td>\n",
       "      <td>115500.000000</td>\n",
       "      <td>107907.593750</td>\n",
       "      <td>124797.092105</td>\n",
       "      <td>139906.025000</td>\n",
       "      <td>141205.366606</td>\n",
       "      <td>141205.366606</td>\n",
       "      <td>98259.482759</td>\n",
       "      <td>106068.842857</td>\n",
       "      <td>98259.482759</td>\n",
       "      <td>98259.482759</td>\n",
       "      <td>97950.833333</td>\n",
       "      <td>98259.482759</td>\n",
       "      <td>98259.482759</td>\n",
       "      <td>114251.531646</td>\n",
       "      <td>157482.765625</td>\n",
       "      <td>144015.982759</td>\n",
       "      <td>115851.396552</td>\n",
       "      <td>135000.000000</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>180689.711340</td>\n",
       "      <td>180689.711340</td>\n",
       "      <td>137735.714286</td>\n",
       "      <td>132375.000000</td>\n",
       "      <td>152211.312500</td>\n",
       "      <td>171289.927536</td>\n",
       "      <td>174235.666667</td>\n",
       "      <td>152149.584906</td>\n",
       "      <td>141668.848101</td>\n",
       "      <td>34900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>373.750000</td>\n",
       "      <td>145160.416667</td>\n",
       "      <td>191112.542888</td>\n",
       "      <td>143409.933333</td>\n",
       "      <td>142922.191142</td>\n",
       "      <td>181009.158212</td>\n",
       "      <td>183253.524157</td>\n",
       "      <td>164502.708844</td>\n",
       "      <td>180625.987666</td>\n",
       "      <td>180846.010283</td>\n",
       "      <td>176705.292941</td>\n",
       "      <td>179862.495455</td>\n",
       "      <td>145902.220339</td>\n",
       "      <td>183885.405113</td>\n",
       "      <td>180922.020690</td>\n",
       "      <td>185725.392457</td>\n",
       "      <td>175539.977547</td>\n",
       "      <td>133553.796238</td>\n",
       "      <td>155660.547619</td>\n",
       "      <td>131510.402477</td>\n",
       "      <td>145548.741497</td>\n",
       "      <td>170921.710497</td>\n",
       "      <td>179436.582897</td>\n",
       "      <td>147198.689024</td>\n",
       "      <td>147184.906250</td>\n",
       "      <td>155107.126961</td>\n",
       "      <td>156067.643564</td>\n",
       "      <td>145264.598080</td>\n",
       "      <td>183378.131707</td>\n",
       "      <td>149658.235644</td>\n",
       "      <td>141287.297348</td>\n",
       "      <td>183242.204589</td>\n",
       "      <td>166756.434896</td>\n",
       "      <td>154560.714286</td>\n",
       "      <td>162693.417544</td>\n",
       "      <td>184622.364905</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>160164.021505</td>\n",
       "      <td>156267.219925</td>\n",
       "      <td>181709.111986</td>\n",
       "      <td>143161.853801</td>\n",
       "      <td>186158.813761</td>\n",
       "      <td>187028.395283</td>\n",
       "      <td>146426.575406</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>124728.476821</td>\n",
       "      <td>165594.030612</td>\n",
       "      <td>180971.578281</td>\n",
       "      <td>134247.150289</td>\n",
       "      <td>162845.735172</td>\n",
       "      <td>176684.049180</td>\n",
       "      <td>183217.146057</td>\n",
       "      <td>140522.540268</td>\n",
       "      <td>155186.517569</td>\n",
       "      <td>183816.463235</td>\n",
       "      <td>141205.366606</td>\n",
       "      <td>141205.366606</td>\n",
       "      <td>135245.717460</td>\n",
       "      <td>138503.159184</td>\n",
       "      <td>142893.495902</td>\n",
       "      <td>127151.030100</td>\n",
       "      <td>129813.178082</td>\n",
       "      <td>187095.717536</td>\n",
       "      <td>187740.811853</td>\n",
       "      <td>186730.340206</td>\n",
       "      <td>157482.765625</td>\n",
       "      <td>144015.982759</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>180689.711340</td>\n",
       "      <td>180689.711340</td>\n",
       "      <td>187571.216981</td>\n",
       "      <td>182078.329759</td>\n",
       "      <td>182034.509821</td>\n",
       "      <td>172689.617647</td>\n",
       "      <td>177176.490421</td>\n",
       "      <td>172945.844946</td>\n",
       "      <td>174634.731682</td>\n",
       "      <td>130000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>749.500000</td>\n",
       "      <td>184802.941860</td>\n",
       "      <td>191112.542888</td>\n",
       "      <td>181337.643952</td>\n",
       "      <td>176590.069705</td>\n",
       "      <td>181009.158212</td>\n",
       "      <td>183253.524157</td>\n",
       "      <td>164502.708844</td>\n",
       "      <td>180625.987666</td>\n",
       "      <td>180846.010283</td>\n",
       "      <td>176705.292941</td>\n",
       "      <td>179862.495455</td>\n",
       "      <td>191182.906250</td>\n",
       "      <td>183885.405113</td>\n",
       "      <td>180922.020690</td>\n",
       "      <td>185725.392457</td>\n",
       "      <td>175539.977547</td>\n",
       "      <td>161542.141447</td>\n",
       "      <td>201734.593272</td>\n",
       "      <td>149604.171756</td>\n",
       "      <td>201707.040000</td>\n",
       "      <td>170921.710497</td>\n",
       "      <td>179436.582897</td>\n",
       "      <td>174903.882979</td>\n",
       "      <td>167843.400000</td>\n",
       "      <td>155107.126961</td>\n",
       "      <td>156067.643564</td>\n",
       "      <td>145264.598080</td>\n",
       "      <td>183378.131707</td>\n",
       "      <td>149658.235644</td>\n",
       "      <td>172441.997654</td>\n",
       "      <td>183242.204589</td>\n",
       "      <td>166756.434896</td>\n",
       "      <td>169607.705382</td>\n",
       "      <td>162693.417544</td>\n",
       "      <td>184622.364905</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>184078.475884</td>\n",
       "      <td>156267.219925</td>\n",
       "      <td>181709.111986</td>\n",
       "      <td>214993.065990</td>\n",
       "      <td>186158.813761</td>\n",
       "      <td>187028.395283</td>\n",
       "      <td>167201.504505</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>167170.805195</td>\n",
       "      <td>165594.030612</td>\n",
       "      <td>180971.578281</td>\n",
       "      <td>213551.978964</td>\n",
       "      <td>162845.735172</td>\n",
       "      <td>180756.652713</td>\n",
       "      <td>183217.146057</td>\n",
       "      <td>140522.540268</td>\n",
       "      <td>160176.227129</td>\n",
       "      <td>183816.463235</td>\n",
       "      <td>211926.204981</td>\n",
       "      <td>206207.696498</td>\n",
       "      <td>202098.170977</td>\n",
       "      <td>154389.937294</td>\n",
       "      <td>203170.442136</td>\n",
       "      <td>184299.762406</td>\n",
       "      <td>176900.146617</td>\n",
       "      <td>187095.717536</td>\n",
       "      <td>187740.811853</td>\n",
       "      <td>186730.340206</td>\n",
       "      <td>157482.765625</td>\n",
       "      <td>173547.204545</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>180689.711340</td>\n",
       "      <td>180689.711340</td>\n",
       "      <td>187571.216981</td>\n",
       "      <td>182078.329759</td>\n",
       "      <td>182034.509821</td>\n",
       "      <td>177840.066667</td>\n",
       "      <td>179548.348178</td>\n",
       "      <td>172945.844946</td>\n",
       "      <td>174634.731682</td>\n",
       "      <td>163000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1108.750000</td>\n",
       "      <td>195744.558824</td>\n",
       "      <td>191112.542888</td>\n",
       "      <td>181337.643952</td>\n",
       "      <td>215854.852814</td>\n",
       "      <td>181009.158212</td>\n",
       "      <td>183253.524157</td>\n",
       "      <td>205860.732323</td>\n",
       "      <td>180625.987666</td>\n",
       "      <td>180846.010283</td>\n",
       "      <td>180211.805556</td>\n",
       "      <td>179862.495455</td>\n",
       "      <td>203897.550459</td>\n",
       "      <td>183885.405113</td>\n",
       "      <td>180922.020690</td>\n",
       "      <td>185725.392457</td>\n",
       "      <td>209656.497238</td>\n",
       "      <td>208597.949020</td>\n",
       "      <td>201734.593272</td>\n",
       "      <td>224159.198813</td>\n",
       "      <td>201707.040000</td>\n",
       "      <td>170921.710497</td>\n",
       "      <td>179436.582897</td>\n",
       "      <td>216476.152913</td>\n",
       "      <td>217430.650873</td>\n",
       "      <td>205957.144970</td>\n",
       "      <td>185895.184080</td>\n",
       "      <td>231702.935567</td>\n",
       "      <td>183378.131707</td>\n",
       "      <td>225829.750000</td>\n",
       "      <td>203596.697959</td>\n",
       "      <td>183242.204589</td>\n",
       "      <td>188960.728261</td>\n",
       "      <td>236226.845455</td>\n",
       "      <td>196331.633333</td>\n",
       "      <td>184622.364905</td>\n",
       "      <td>181949.470919</td>\n",
       "      <td>184078.475884</td>\n",
       "      <td>191747.124324</td>\n",
       "      <td>181709.111986</td>\n",
       "      <td>214993.065990</td>\n",
       "      <td>186158.813761</td>\n",
       "      <td>187028.395283</td>\n",
       "      <td>188132.816254</td>\n",
       "      <td>170728.974203</td>\n",
       "      <td>180556.807198</td>\n",
       "      <td>199700.782235</td>\n",
       "      <td>202420.555085</td>\n",
       "      <td>180971.578281</td>\n",
       "      <td>213551.978964</td>\n",
       "      <td>210840.615561</td>\n",
       "      <td>180756.652713</td>\n",
       "      <td>183217.146057</td>\n",
       "      <td>213800.800866</td>\n",
       "      <td>212176.350649</td>\n",
       "      <td>183816.463235</td>\n",
       "      <td>211926.204981</td>\n",
       "      <td>226208.555932</td>\n",
       "      <td>202098.170977</td>\n",
       "      <td>216624.805930</td>\n",
       "      <td>203170.442136</td>\n",
       "      <td>184299.762406</td>\n",
       "      <td>176900.146617</td>\n",
       "      <td>187095.717536</td>\n",
       "      <td>187740.811853</td>\n",
       "      <td>186730.340206</td>\n",
       "      <td>219441.075075</td>\n",
       "      <td>221545.352423</td>\n",
       "      <td>187096.857573</td>\n",
       "      <td>180269.684896</td>\n",
       "      <td>178939.159173</td>\n",
       "      <td>180689.711340</td>\n",
       "      <td>180689.711340</td>\n",
       "      <td>187571.216981</td>\n",
       "      <td>182078.329759</td>\n",
       "      <td>182034.509821</td>\n",
       "      <td>185865.387755</td>\n",
       "      <td>182698.573123</td>\n",
       "      <td>172945.844946</td>\n",
       "      <td>174634.731682</td>\n",
       "      <td>215000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>239938.773663</td>\n",
       "      <td>212604.693878</td>\n",
       "      <td>244774.580000</td>\n",
       "      <td>252893.385185</td>\n",
       "      <td>181009.158212</td>\n",
       "      <td>183253.524157</td>\n",
       "      <td>236606.837838</td>\n",
       "      <td>214412.765625</td>\n",
       "      <td>180846.010283</td>\n",
       "      <td>221899.447368</td>\n",
       "      <td>204379.230769</td>\n",
       "      <td>315236.573770</td>\n",
       "      <td>183885.405113</td>\n",
       "      <td>180922.020690</td>\n",
       "      <td>185725.392457</td>\n",
       "      <td>209656.497238</td>\n",
       "      <td>294733.390625</td>\n",
       "      <td>201734.593272</td>\n",
       "      <td>351299.222222</td>\n",
       "      <td>336963.500000</td>\n",
       "      <td>218227.529661</td>\n",
       "      <td>252294.090909</td>\n",
       "      <td>225259.960000</td>\n",
       "      <td>229469.960000</td>\n",
       "      <td>264303.866071</td>\n",
       "      <td>367492.000000</td>\n",
       "      <td>367868.461538</td>\n",
       "      <td>183378.131707</td>\n",
       "      <td>225829.750000</td>\n",
       "      <td>326039.521277</td>\n",
       "      <td>219090.272727</td>\n",
       "      <td>256582.962264</td>\n",
       "      <td>236226.845455</td>\n",
       "      <td>348715.769231</td>\n",
       "      <td>184622.364905</td>\n",
       "      <td>222723.214286</td>\n",
       "      <td>386965.500000</td>\n",
       "      <td>429986.714286</td>\n",
       "      <td>181709.111986</td>\n",
       "      <td>214993.065990</td>\n",
       "      <td>186158.813761</td>\n",
       "      <td>187028.395283</td>\n",
       "      <td>439343.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "      <td>475000.000000</td>\n",
       "      <td>509937.500000</td>\n",
       "      <td>204478.700000</td>\n",
       "      <td>180971.578281</td>\n",
       "      <td>307593.548387</td>\n",
       "      <td>210840.615561</td>\n",
       "      <td>219440.438202</td>\n",
       "      <td>183217.146057</td>\n",
       "      <td>323132.653846</td>\n",
       "      <td>283971.025000</td>\n",
       "      <td>183816.463235</td>\n",
       "      <td>251666.666667</td>\n",
       "      <td>226208.555932</td>\n",
       "      <td>250618.554054</td>\n",
       "      <td>326795.842105</td>\n",
       "      <td>236088.698246</td>\n",
       "      <td>308849.062500</td>\n",
       "      <td>461525.666667</td>\n",
       "      <td>214340.000000</td>\n",
       "      <td>187740.811853</td>\n",
       "      <td>186730.340206</td>\n",
       "      <td>219441.075075</td>\n",
       "      <td>221545.352423</td>\n",
       "      <td>220297.375000</td>\n",
       "      <td>301250.000000</td>\n",
       "      <td>217936.589286</td>\n",
       "      <td>215492.500000</td>\n",
       "      <td>215492.500000</td>\n",
       "      <td>187571.216981</td>\n",
       "      <td>182078.329759</td>\n",
       "      <td>182034.509821</td>\n",
       "      <td>199738.489796</td>\n",
       "      <td>187530.407692</td>\n",
       "      <td>280094.270833</td>\n",
       "      <td>278236.510204</td>\n",
       "      <td>755000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id     MSSubClass       MSZoning    LotFrontage  \\\n",
       "count  1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean    738.685788  180808.898973  180808.898973  180808.898973   \n",
       "std     421.609683   38480.990758   25720.477372   33736.046396   \n",
       "min       1.000000   96493.392857  107592.250000  143409.933333   \n",
       "25%     373.750000  145160.416667  191112.542888  143409.933333   \n",
       "50%     749.500000  184802.941860  191112.542888  181337.643952   \n",
       "75%    1108.750000  195744.558824  191112.542888  181337.643952   \n",
       "max    1460.000000  239938.773663  212604.693878  244774.580000   \n",
       "\n",
       "             LotArea         Street          Alley       LotShape  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     37115.680039    3055.507893   11140.998208   21889.220846   \n",
       "min    142922.191142  134228.600000  121154.162162  164502.708844   \n",
       "25%    142922.191142  181009.158212  183253.524157  164502.708844   \n",
       "50%    176590.069705  181009.158212  183253.524157  164502.708844   \n",
       "75%    215854.852814  181009.158212  183253.524157  205860.732323   \n",
       "max    252893.385185  181009.158212  183253.524157  236606.837838   \n",
       "\n",
       "         LandContour      Utilities      LotConfig      LandSlope  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     11298.306442    1268.316692   11199.613349    3929.532340   \n",
       "min    141651.720000  137500.000000  128000.000000  179862.495455   \n",
       "25%    180625.987666  180846.010283  176705.292941  179862.495455   \n",
       "50%    180625.987666  180846.010283  176705.292941  179862.495455   \n",
       "75%    180625.987666  180846.010283  180211.805556  179862.495455   \n",
       "max    214412.765625  180846.010283  221899.447368  204379.230769   \n",
       "\n",
       "        Neighborhood     Condition1     Condition2       BldgType  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     45860.552169    9128.523689    1362.749348   14168.223861   \n",
       "min    124453.375000  145369.306452  164406.250000  134357.500000   \n",
       "25%    145902.220339  183885.405113  180922.020690  185725.392457   \n",
       "50%    191182.906250  183885.405113  180922.020690  185725.392457   \n",
       "75%    203897.550459  183885.405113  180922.020690  185725.392457   \n",
       "max    315236.573770  183885.405113  180922.020690  185725.392457   \n",
       "\n",
       "          HouseStyle    OverallQual    OverallCond      YearBuilt  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     22145.611412   55164.787624   24583.702981   49618.871670   \n",
       "min    142481.121951  110298.527473  123138.166667  131510.402477   \n",
       "25%    175539.977547  133553.796238  155660.547619  131510.402477   \n",
       "50%    175539.977547  161542.141447  201734.593272  149604.171756   \n",
       "75%    209656.497238  208597.949020  201734.593272  224159.198813   \n",
       "max    209656.497238  294733.390625  201734.593272  351299.222222   \n",
       "\n",
       "        YearRemodAdd      RoofStyle       RoofMatl    Exterior1st  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     43963.319552   18957.714577    9908.801138   31675.032788   \n",
       "min    101331.000000  170921.710497  179436.582897  145071.171429   \n",
       "25%    145548.741497  170921.710497  179436.582897  147198.689024   \n",
       "50%    201707.040000  170921.710497  179436.582897  174903.882979   \n",
       "75%    201707.040000  170921.710497  179436.582897  216476.152913   \n",
       "max    336963.500000  218227.529661  252294.090909  225259.960000   \n",
       "\n",
       "         Exterior2nd     MasVnrType     MasVnrArea      ExterQual  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     31237.129369   35361.242793   39197.954278   53953.879151   \n",
       "min    145788.491124  155107.126961  156067.643564   86607.750000   \n",
       "25%    147184.906250  155107.126961  156067.643564  145264.598080   \n",
       "50%    167843.400000  155107.126961  156067.643564  145264.598080   \n",
       "75%    217430.650873  205957.144970  185895.184080  231702.935567   \n",
       "max    229469.960000  264303.866071  367492.000000  367868.461538   \n",
       "\n",
       "           ExterCond     Foundation       BsmtQual       BsmtCond  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     11793.776051   40189.096743   53501.729438   18860.218446   \n",
       "min    108143.586207  123795.269231  110267.928571  101338.466667   \n",
       "25%    183378.131707  149658.235644  141287.297348  183242.204589   \n",
       "50%    183378.131707  149658.235644  172441.997654  183242.204589   \n",
       "75%    183378.131707  225829.750000  203596.697959  183242.204589   \n",
       "max    183378.131707  225829.750000  326039.521277  219090.272727   \n",
       "\n",
       "        BsmtExposure   BsmtFinType1     BsmtFinSF1   BsmtFinType2  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     29723.844614   36637.286166   40718.777833   10001.037180   \n",
       "min    104005.500000  104005.500000  162693.417544  148912.940476   \n",
       "25%    166756.434896  154560.714286  162693.417544  184622.364905   \n",
       "50%    166756.434896  169607.705382  162693.417544  184622.364905   \n",
       "75%    188960.728261  236226.845455  196331.633333  184622.364905   \n",
       "max    256582.962264  236226.845455  348715.769231  184622.364905   \n",
       "\n",
       "          BsmtFinSF2      BsmtUnfSF    TotalBsmtSF        Heating  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std      9020.532512   27800.843566   53928.565770    6089.538646   \n",
       "min    152023.418919   84500.000000  118811.691358  139651.160000   \n",
       "25%    181949.470919  160164.021505  156267.219925  181709.111986   \n",
       "50%    181949.470919  184078.475884  156267.219925  181709.111986   \n",
       "75%    181949.470919  184078.475884  191747.124324  181709.111986   \n",
       "max    222723.214286  386965.500000  429986.714286  181709.111986   \n",
       "\n",
       "           HeatingQC     CentralAir     Electrical       1stFlrSF  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     35198.761059   20007.775506   19586.684249   51714.876682   \n",
       "min     87000.000000  106047.269231  108605.269231  114588.758621   \n",
       "25%    143161.853801  186158.813761  187028.395283  146426.575406   \n",
       "50%    214993.065990  186158.813761  187028.395283  167201.504505   \n",
       "75%    214993.065990  186158.813761  187028.395283  188132.816254   \n",
       "max    214993.065990  186158.813761  187028.395283  439343.000000   \n",
       "\n",
       "            2ndFlrSF   LowQualFinSF      GrLivArea   BsmtFullBath  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     43508.967190    8615.492267   57496.683423   18160.012243   \n",
       "min    116307.896104  180556.807198   87380.861111  165594.030612   \n",
       "25%    170728.974203  180556.807198  124728.476821  165594.030612   \n",
       "50%    170728.974203  180556.807198  167170.805195  165594.030612   \n",
       "75%    170728.974203  180556.807198  199700.782235  202420.555085   \n",
       "max    755000.000000  475000.000000  509937.500000  204478.700000   \n",
       "\n",
       "        BsmtHalfBath       FullBath       HalfBath   BedroomAbvGr  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std      1637.443705   44259.676776   23230.103527   18963.116711   \n",
       "min    127500.000000  134247.150289  162845.735172  157600.802817   \n",
       "25%    180971.578281  134247.150289  162845.735172  176684.049180   \n",
       "50%    180971.578281  213551.978964  162845.735172  180756.652713   \n",
       "75%    180971.578281  213551.978964  210840.615561  180756.652713   \n",
       "max    180971.578281  307593.548387  210840.615561  219440.438202   \n",
       "\n",
       "        KitchenAbvGr    KitchenQual   TotRmsAbvGrd     Functional  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     11184.030580   52529.934030   39490.667509   11096.100031   \n",
       "min    115500.000000  107907.593750  124797.092105  139906.025000   \n",
       "25%    183217.146057  140522.540268  155186.517569  183816.463235   \n",
       "50%    183217.146057  140522.540268  160176.227129  183816.463235   \n",
       "75%    183217.146057  213800.800866  212176.350649  183816.463235   \n",
       "max    183217.146057  323132.653846  283971.025000  183816.463235   \n",
       "\n",
       "          Fireplaces    FireplaceQu     GarageType    GarageYrBlt  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     38140.877100   38095.625026   38765.020398   47983.619169   \n",
       "min    141205.366606  141205.366606   98259.482759  106068.842857   \n",
       "25%    141205.366606  141205.366606  135245.717460  138503.159184   \n",
       "50%    211926.204981  206207.696498  202098.170977  154389.937294   \n",
       "75%    211926.204981  226208.555932  202098.170977  216624.805930   \n",
       "max    251666.666667  226208.555932  250618.554054  326795.842105   \n",
       "\n",
       "        GarageFinish     GarageCars     GarageArea     GarageQual  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     42784.463581   55753.813144   54314.602977   22515.160301   \n",
       "min     98259.482759   98259.482759   97950.833333   98259.482759   \n",
       "25%    142893.495902  127151.030100  129813.178082  187095.717536   \n",
       "50%    203170.442136  184299.762406  176900.146617  187095.717536   \n",
       "75%    203170.442136  184299.762406  176900.146617  187095.717536   \n",
       "max    236088.698246  308849.062500  461525.666667  214340.000000   \n",
       "\n",
       "          GarageCond     PavedDrive     WoodDeckSF    OpenPorchSF  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     22447.174795   19383.388123   27523.829396   35638.561525   \n",
       "min     98259.482759  114251.531646  157482.765625  144015.982759   \n",
       "25%    187740.811853  186730.340206  157482.765625  144015.982759   \n",
       "50%    187740.811853  186730.340206  157482.765625  173547.204545   \n",
       "75%    187740.811853  186730.340206  219441.075075  221545.352423   \n",
       "max    187740.811853  186730.340206  219441.075075  221545.352423   \n",
       "\n",
       "       EnclosedPorch      3SsnPorch    ScreenPorch       PoolArea  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std     18039.742483    6238.054701    8335.382737    2034.059631   \n",
       "min    115851.396552  135000.000000  178939.159173  180689.711340   \n",
       "25%    187096.857573  180269.684896  178939.159173  180689.711340   \n",
       "50%    187096.857573  180269.684896  178939.159173  180689.711340   \n",
       "75%    187096.857573  180269.684896  178939.159173  180689.711340   \n",
       "max    220297.375000  301250.000000  217936.589286  215492.500000   \n",
       "\n",
       "              PoolQC          Fence    MiscFeature        MiscVal  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std      2034.059631   16127.286579    6184.091203    5922.798839   \n",
       "min    180689.711340  137735.714286  132375.000000  152211.312500   \n",
       "25%    180689.711340  187571.216981  182078.329759  182034.509821   \n",
       "50%    180689.711340  187571.216981  182078.329759  182034.509821   \n",
       "75%    180689.711340  187571.216981  182078.329759  182034.509821   \n",
       "max    215492.500000  187571.216981  182078.329759  182034.509821   \n",
       "\n",
       "              MoSold         YrSold       SaleType  SaleCondition  \\\n",
       "count    1168.000000    1168.000000    1168.000000    1168.000000   \n",
       "mean   180808.898973  180808.898973  180808.898973  180808.898973   \n",
       "std      7697.828418    4423.740972   30036.572861   30678.760855   \n",
       "min    171289.927536  174235.666667  152149.584906  141668.848101   \n",
       "25%    172689.617647  177176.490421  172945.844946  174634.731682   \n",
       "50%    177840.066667  179548.348178  172945.844946  174634.731682   \n",
       "75%    185865.387755  182698.573123  172945.844946  174634.731682   \n",
       "max    199738.489796  187530.407692  280094.270833  278236.510204   \n",
       "\n",
       "           SalePrice  LotFrontage_NA  GarageYrBlt_NA  \n",
       "count    1168.000000     1168.000000     1168.000000  \n",
       "mean   180808.898973        0.181507        0.049658  \n",
       "std     78499.911304        0.385603        0.217329  \n",
       "min     34900.000000        0.000000        0.000000  \n",
       "25%    130000.000000        0.000000        0.000000  \n",
       "50%    163000.000000        0.000000        0.000000  \n",
       "75%    215000.000000        0.000000        0.000000  \n",
       "max    755000.000000        1.000000        1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_vars = [var for var in X_train.columns if var not in ['Id', 'SalePrice']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit scaler\n",
    "scaler = StandardScaler() # create an instance\n",
    "scaler.fit(X_train[training_vars]) #  fit  the scaler to the train set for later use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scaler is now ready, we can use it in a machine learning algorithm when required. See below.\n",
    "\n",
    "### Machine Learning algorithm building\n",
    "\n",
    "#### xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb train mse: 252828273.12099028\n",
      "xgb test mse: 783577806.7781161\n"
     ]
    }
   ],
   "source": [
    "xgb_model = xgb.XGBRegressor()\n",
    "\n",
    "eval_set = [(X_test[training_vars], y_test)]\n",
    "xgb_model.fit(X_train[training_vars], y_train, eval_set=eval_set, verbose=False)\n",
    "\n",
    "pred = xgb_model.predict(X_train[training_vars])\n",
    "print('xgb train mse: {}'.format(mean_squared_error(y_train, pred)))\n",
    "pred = xgb_model.predict(X_test[training_vars])\n",
    "print('xgb test mse: {}'.format(mean_squared_error(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf train mse: 158817040.80635273\n",
      "rf test mse: 937600451.5399315\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor()\n",
    "rf_model.fit(X_train[training_vars], y_train)\n",
    "\n",
    "pred = rf_model.predict(X_train[training_vars])\n",
    "print('rf train mse: {}'.format(mean_squared_error(y_train, pred)))\n",
    "pred = rf_model.predict(X_test[training_vars])\n",
    "print('rf test mse: {}'.format(mean_squared_error(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support vector machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR train mse: 6466993596.796061\n",
      "SVR test mse: 7236778915.478487\n"
     ]
    }
   ],
   "source": [
    "SVR_model = SVR()\n",
    "SVR_model.fit(scaler.transform(X_train[training_vars]), y_train)\n",
    "\n",
    "pred = SVR_model.predict(scaler.transform(X_train[training_vars]))\n",
    "print('SVR train mse: {}'.format(mean_squared_error(y_train, pred)))\n",
    "pred = SVR_model.predict(scaler.transform(X_test[training_vars]))\n",
    "print('SVR test mse: {}'.format(mean_squared_error(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regularised linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear train mse: 583103463.5841156\n",
      "linear test mse: 1278856185.86032\n"
     ]
    }
   ],
   "source": [
    "lin_model = Lasso(random_state=2909)\n",
    "lin_model.fit(scaler.transform(X_train[training_vars]), y_train)\n",
    "\n",
    "pred = lin_model.predict(scaler.transform(X_train[training_vars]))\n",
    "print('linear train mse: {}'.format(mean_squared_error(y_train, pred)))\n",
    "pred = lin_model.predict(scaler.transform(X_test[training_vars]))\n",
    "print('linear test mse: {}'.format(mean_squared_error(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submission to Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_ls = []\n",
    "for model in [xgb_model, rf_model]:\n",
    "    pred_ls.append(pd.Series(model.predict(submission[training_vars])))\n",
    "\n",
    "pred = SVR_model.predict(scaler.transform(submission[training_vars]))\n",
    "pred_ls.append(pd.Series(pred))\n",
    "\n",
    "pred = lin_model.predict(scaler.transform(submission[training_vars]))\n",
    "pred_ls.append(pd.Series(pred))\n",
    "\n",
    "final_pred = pd.concat(pred_ls, axis=1).mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>128512.028897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>164614.202198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>175251.148672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>178200.129918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>178829.746987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  128512.028897\n",
       "1  1462  164614.202198\n",
       "2  1463  175251.148672\n",
       "3  1464  178200.129918\n",
       "4  1465  178829.746987"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat([submission.Id, final_pred], axis=1)\n",
    "temp.columns = ['Id', 'SalePrice']\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp.to_csv('submit_housesale.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x461da48908>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABCIAAAGuCAYAAABFm6dXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ny7+5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461d4ad550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = pd.Series(rf_model.feature_importances_)\n",
    "importance.index = training_vars\n",
    "importance.sort_values(inplace=True, ascending=False)\n",
    "importance.plot.bar(figsize=(18,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x461dfcd828>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBsAAAGuCAYAAADRfQ6lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUvl/gjHkX/5C+uoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461d971cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = pd.Series(xgb_model.feature_importances_)\n",
    "importance.index = training_vars\n",
    "importance.sort_values(inplace=True, ascending=False)\n",
    "importance.plot.bar(figsize=(18,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x461db0c6a0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABCUAAAGuCAYAAACnR7wuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x461e3927f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = pd.Series(np.abs(lin_model.coef_.ravel()))\n",
    "importance.index = training_vars\n",
    "importance.sort_values(inplace=True, ascending=False)\n",
    "importance.plot.bar(figsize=(18,6))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {
    "height": "583px",
    "left": "0px",
    "right": "1324px",
    "top": "107px",
    "width": "212px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
